Nucleus image segmentation is a crucial step in the analysis, pathological diagnosis, and classification, which heavily relies on the quality of nucleus segmentation. However, the complexity of issues such as variations in nucleus size, blurred nucleus contours, uneven staining, cell clustering, and overlapping cells poses significant challenges. Current methods for nucleus segmentation primarily rely on nuclear morphology or contour-based approaches. Nuclear morphology-based methods exhibit limited generalization ability and struggle to effectively predict irregular-shaped nuclei, while contour-based extraction methods face challenges in accurately segmenting overlapping nuclei. To address the aforementioned issues, we propose a dual-branch network using hybrid attention based residual U-blocks for nucleus instance segmentation. The network simultaneously predicts target information and target contours. Additionally, we introduce a post-processing method that combines the target information and target contours to distinguish overlapping nuclei and generate an instance segmentation image. Within the network, we propose a context fusion block (CF-block) that effectively extracts and merges contextual information from the network. Extensive quantitative evaluations are conducted to assess the performance of our method. Experimental results demonstrate the superior performance of the proposed method compared to state-of-the-art approaches on the BNS, MoNuSeg, CoNSeg, and CPM-17 datasets.

Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are available for new tissues/organs. Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain. As a source domain, redundant features that are not conducive to the task will be extracted. Migration between ultrasound images can avoid this problem, but there are few types of public datasets, and it is difficult to find sufficiently similar source domains. Compared with natural images, ultrasound images have less information, and there are fewer transferable features between different ultrasound images, which may cause negative transfer. To this end, a multi-source adversarial transfer learning network for ultrasound image segmentation is proposed. Specifically, to address the lack of annotations, the idea of adversarial transfer learning is used to adaptively extract common features between a certain pair of source and target domains, which provides the possibility to utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a single source domain, multi-source transfer learning is adopted to fuse knowledge from multiple source domains. In order to ensure the effectiveness of the fusion and maximize the use of precious data, a multi-source domain independent strategy is also proposed to improve the estimation of the target domain data distribution, which further increases the learning ability of the multi-source adversarial migration learning network in multiple domains.

This paper asks whether current self-supervised learning methods, if sufficiently scaled up, would be able to reach human-level visual object recognition capabilities with the same type and amount of visual experience humans learn from. Previous work on this question only considered the scaling of data size. Here, we consider the simultaneous scaling of data size, model size, and image resolution. We perform a scaling experiment with vision transformers up to 633M parameters in size (ViT-H/14) trained with up to 5K hours of human-like video data (long, continuous, mostly egocentric videos) with image resolutions of up to 476x476 pixels. The efficiency of masked autoencoders (MAEs) as a self-supervised learning algorithm makes it possible to run this scaling experiment on an unassuming academic budget. We find that it is feasible to reach human-level object recognition capacity at sub-human scales of model size, data size, and image size, if these factors are scaled up simultaneously. To give a concrete example, we estimate that a 2.5B parameter ViT model trained with 20K hours (2.3 years) of human-like video data with a spatial resolution of 952x952 pixels should be able to reach roughly human-level accuracy on ImageNet. Human-level competence is thus achievable for a fundamental perceptual capability from human-like perceptual experience (human-like in both amount and type) with extremely generic learning algorithms and architectures and without any substantive inductive biases.

With the increasing volume of astronomical data generated by modern survey telescopes, automated pipelines and machine learning techniques have become crucial for analyzing and extracting knowledge from these datasets. Anomaly detection, i.e. the task of identifying irregular or unexpected patterns in the data, is a complex challenge in astronomy. In this paper, we propose Multi-Class Deep Support Vector Data Description (MCDSVDD), an extension of the state-of-the-art anomaly detection algorithm One-Class Deep SVDD, specifically designed to handle different inlier categories with distinct data distributions. MCDSVDD uses a neural network to map the data into hyperspheres, where each hypersphere represents a specific inlier category. The distance of each sample from the centers of these hyperspheres determines the anomaly score. We evaluate the effectiveness of MCDSVDD by comparing its performance with several anomaly detection algorithms on a large dataset of astronomical light-curves obtained from the Zwicky Transient Facility. Our results demonstrate the efficacy of MCDSVDD in detecting anomalous sources while leveraging the presence of different inlier categories. The code and the data needed to reproduce our results are publicly available at https://github.com/mperezcarrasco/AnomalyALeRCE.

Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing DA methods suffer from a substantial drop in performance when applied to a multi-class training setting, due to the co-existence of low-quality pseudo labels and class imbalance issues. In this paper, we address this challenge by proposing a novel ReDB framework tailored for learning to detect all classes at once. Our approach produces Reliable, Diverse, and class-Balanced pseudo 3D boxes to iteratively guide the self-training on a distributionally different target domain. To alleviate disruptions caused by the environmental discrepancy (e.g., beam numbers), the proposed cross-domain examination (CDE) assesses the correctness of pseudo labels by copy-pasting target instances into a source environment and measuring the prediction consistency. To reduce computational overhead and mitigate the object shift (e.g., scales and point densities), we design an overlapped boxes counting (OBC) metric that allows to uniformly downsample pseudo-labeled objects across different geometric characteristics. To confront the issue of inter-class imbalance, we progressively augment the target point clouds with a class-balanced set of pseudo-labeled target instances and source objects, which boosts recognition accuracies on both frequently appearing and rare classes. Experimental results on three benchmark datasets using both voxel-based (i.e., SECOND) and point-based 3D detectors (i.e., PointRCNN) demonstrate that our proposed ReDB approach outperforms existing 3D domain adaptation methods by a large margin, improving 23.15% mAP on the nuScenes $\rightarrow$ KITTI task. The code is available at https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet.

Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vectors for phishing attacks because they are widely regarded as trustworthy data resources, and are accessible across different platforms. Therefore, researchers have developed many different PDF malware detection methods. Performance in detecting PDF malware is greatly influenced by feature selection. In this research, we propose a small features set that don't require too much domain knowledge of the PDF file. We evaluate proposed features with six different machine learning models. We report the best accuracy of 99.75% when using Random Forest model. Our proposed feature set, which consists of just 12 features, is one of the most conciseness in the field of PDF malware detection. Despite its modest size, we obtain comparable results to state-of-the-art that employ a much larger set of features.

Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ($C_n^2$) is highly relevant for the successful development and deployment of future free-space optical communication links. In this letter, we propose a physics-informed machine learning (ML) methodology, $\Pi$-ML, based on dimensional analysis and gradient boosting to estimate $C_n^2$. Through a systematic feature importance analysis, we identify the normalized variance of potential temperature as the dominating feature for predicting $C_n^2$. For statistical robustness, we train an ensemble of models which yields high performance on the out-of-sample data of $R^2=0.958\pm0.001$.

A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represent a more efficient state preparation procedure than current methods. This methodology has the potential to speed-up algorithms, such as Monte Carlo analysis. In particular, after demonstrating the effectiveness of the network in the learning task, the technique is applied to price Asian option derivatives, providing the foundation for further research on other path-dependent options.

Autonomous experimentation has emerged as an efficient approach to accelerate the pace of materials discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of emission lines monitored during the co-sputtering from elemental Zn and Ti targets in N$_2$ atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined composition, by minimizing the absolute error between desired and measured emission signals. We validated our approach by autonomously fabricating Zn$_x$Ti$_{1-x}$N$_y$ films with deviations from the targeted cation composition within relative 3.5 %, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity depends non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature.

The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that enhance this ability. However, these methods do not fully exploit the answers generated by the LLM to guide subsequent responses. This paper proposes a new prompting method, named Progressive-Hint Prompting (PHP), that enables automatic multiple interactions between users and LLMs by using previously generated answers as hints to progressively guide toward the correct answers. PHP is orthogonal to CoT and self-consistency, making it easy to combine with state-of-the-art techniques to further improve performance. We conducted extensive and comprehensive experiments on seven benchmarks. The results show that PHP significantly improves accuracy while remaining highly efficient. For instance, with text-davinci-003, we observed a 4.2% improvement on GSM8K with greedy decoding compared to Complex CoT, and a 46.17% reduction in sample paths with self-consistency. With GPT-4 and PHP, we achieve state-of-the-art performances on SVAMP (89.1% -> 91.9%), GSM8K (92% -> 95.5%), AQuA (76.4% -> 79.9%) and MATH (50.3% -> 53.9%).

Promotions play a crucial role in e-commerce platforms, and various cost structures are employed to drive user engagement. This paper focuses on promotions with response-dependent costs, where expenses are incurred only when a purchase is made. Such promotions include discounts and coupons. While existing uplift model approaches aim to address this challenge, these approaches often necessitate training multiple models, like meta-learners, or encounter complications when estimating profit due to zero-inflated values stemming from non-converted individuals with zero cost and profit. To address these challenges, we introduce Incremental Profit per Conversion (IPC), a novel uplift measure of promotional campaigns' efficiency in unit economics. Through a proposed response transformation, we demonstrate that IPC requires only converted data, its propensity, and a single model to be estimated. As a result, IPC resolves the issues mentioned above while mitigating the noise typically associated with the class imbalance in conversion datasets and biases arising from the many-to-one mapping between search and purchase data. Lastly, we validate the efficacy of our approach by presenting results obtained from a synthetic simulation of a discount coupon campaign.

Random Search is one of the most widely-used method for Hyperparameter Optimization, and is critical to the success of deep learning models. Despite its astonishing performance, little non-heuristic theory has been developed to describe the underlying working mechanism. This paper gives a theoretical accounting of Random Search. We introduce the concept of \emph{scattering dimension} that describes the landscape of the underlying function, and quantifies the performance of random search. We show that, when the environment is noise-free, the output of random search converges to the optimal value in probability at rate $ \widetilde{\mathcal{O}} \left( \left( \frac{1}{T} \right)^{ \frac{1}{d_s} } \right) $, where $ d_s \ge 0 $ is the scattering dimension of the underlying function. When the observed function values are corrupted by bounded $iid$ noise, the output of random search converges to the optimal value in probability at rate $ \widetilde{\mathcal{O}} \left( \left( \frac{1}{T} \right)^{ \frac{1}{d_s + 1} } \right) $. In addition, based on the principles of random search, we introduce an algorithm, called BLiN-MOS, for Lipschitz bandits in doubling metric spaces that are also endowed with a probability measure, and show that BLiN-MOS achieves a regret rate of order $ \widetilde{\mathcal{O}} \left( T^{ \frac{d_z}{d_z + 1} } \right) $, where $d_z$ is the zooming dimension of the problem instance.

Large pre-trained transformers are show-stealer in modern-day deep learning, and it becomes crucial to comprehend the parsimonious patterns that exist within them as they grow in scale. With exploding parameter counts, Lottery Ticket Hypothesis (LTH) and its variants, have lost their pragmatism in sparsifying them due to high computation and memory bottleneck of repetitive train-prune-retrain routine of iterative magnitude pruning (IMP) which worsens with increasing model size. This paper comprehensively studies induced sparse patterns across multiple large pre-trained vision and language transformers. We propose the existence of -- essential sparsity defined with a sharp dropping point beyond which the performance declines much faster w.r.t the rise of sparsity level, when we directly remove weights with the smallest magnitudes in one-shot without re-training. We also find essential sparsity to hold valid for N:M sparsity patterns as well as on modern-scale large language models (Vicuna-7B). We also present an intriguing emerging phenomenon of abrupt sparsification during the pre-training of BERT, i.e., BERT suddenly becomes heavily sparse in pre-training after certain iterations. Moreover, our observations also indicate a counter-intuitive finding that BERT trained with a larger amount of pre-training data tends to have a better ability to condense knowledge in comparatively relatively fewer parameters. Lastly, we investigate the effect of the pre-training loss on essential sparsity and discover that self-supervised learning (SSL) objectives trigger stronger emergent sparsification properties than supervised learning (SL). Our codes are available at \url{https://github.com/VITA-Group/essential_sparsity}.

This paper uses symmetry to make Convolutional Neural Network classifiers (CNNs) robust against adversarial perturbation attacks. Such attacks add perturbation to original images to generate adversarial images that fool classifiers such as road sign classifiers of autonomous vehicles. Although symmetry is a pervasive aspect of the natural world, CNNs are unable to handle symmetry well. For example, a CNN can classify an image differently from its mirror image. For an adversarial image that misclassifies with a wrong label $l_w$, CNN inability to handle symmetry means that a symmetric adversarial image can classify differently from the wrong label $l_w$. Further than that, we find that the classification of a symmetric adversarial image reverts to the correct label. To classify an image when adversaries are unaware of the defense, we apply symmetry to the image and use the classification label of the symmetric image. To classify an image when adversaries are aware of the defense, we use mirror symmetry and pixel inversion symmetry to form a symmetry group. We apply all the group symmetries to the image and decide on the output label based on the agreement of any two of the classification labels of the symmetry images. Adaptive attacks fail because they need to rely on loss functions that use conflicting CNN output values for symmetric images. Without attack knowledge, the proposed symmetry defense succeeds against both gradient-based and random-search attacks, with up to near-default accuracies for ImageNet. The defense even improves the classification accuracy of original images.

In a fashion e-commerce platform where customers can't physically examine the products on their own, being able to see other customers' text and image reviews of the product is critical while making purchase decisions. Given the high reliance on these reviews, over the years we have observed customers proactively sharing their reviews. With an increase in the coverage of User Generated Content (UGC), there has been a corresponding increase in the number of customer images. It is thus imperative to display the most relevant images on top as it may influence users' online shopping choices and behavior. In this paper, we propose a simple yet effective training procedure for ranking customer images. We created a dataset consisting of Myntra (A Major Indian Fashion e-commerce company) studio posts and highly engaged (upvotes/downvotes) UGC images as our starting point and used selected distortion techniques on the images of the above dataset to bring their quality at par with those of bad UGC images. We train our network to rank bad-quality images lower than high-quality ones. Our proposed method outperforms the baseline models on two metrics, namely correlation coefficient, and accuracy, by substantial margins.  ( 2 min )

This paper surveys some recent developments in measures of association related to a new coefficient of correlation introduced by the author. A straightforward extension of this coefficient to standard Borel spaces (which includes all Polish spaces), overlooked in the literature so far, is proposed at the end of the survey.

The state-of-the-art predictive maintenance (PdM) techniques have shown great success in reducing maintenance costs and downtime of complicated machines while increasing overall productivity through extensive utilization of Internet-of-Things (IoT) and Deep Learning (DL). Unfortunately, IoT sensors and DL algorithms are both prone to cyber-attacks. For instance, DL algorithms are known for their susceptibility to adversarial examples. Such adversarial attacks are vastly under-explored in the PdM domain. This is because the adversarial attacks in the computer vision domain for classification tasks cannot be directly applied to the PdM domain for multivariate time series (MTS) regression tasks. In this work, we propose an end-to-end methodology to design adversarially robust PdM systems by extensively analyzing the effect of different types of adversarial attacks and proposing a novel adversarial defense technique for DL-enabled PdM models. First, we propose novel MTS Projected Gradient Descent (PGD) and MTS PGD with random restarts (PGD_r) attacks. Then, we evaluate the impact of MTS PGD and PGD_r along with MTS Fast Gradient Sign Method (FGSM) and MTS Basic Iterative Method (BIM) on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Bi-directional LSTM based PdM system. Our results using NASA's turbofan engine dataset show that adversarial attacks can cause a severe defect (up to 11X) in the RUL prediction, outperforming the effectiveness of the state-of-the-art PdM attacks by 3X. Furthermore, we present a novel approximate adversarial training method to defend against adversarial attacks. We observe that approximate adversarial training can significantly improve the robustness of PdM models (up to 54X) and outperforms the state-of-the-art PdM defense methods by offering 3X more robustness.

We show that taking the width and depth to infinity in a deep neural network with skip connections, when branches are scaled by $1/\sqrt{depth}$ (the only nontrivial scaling), result in the same covariance structure no matter how that limit is taken. This explains why the standard infinite-width-then-depth approach provides practical insights even for networks with depth of the same order as width. We also demonstrate that the pre-activations, in this case, have Gaussian distributions which has direct applications in Bayesian deep learning. We conduct extensive simulations that show an excellent match with our theoretical findings.

Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter $\epsilon$ needs to be to protect against certain privacy risks in practice is still not well-understood. In this work, we study data reconstruction attacks for discrete data and analyze it under the framework of multiple hypothesis testing. We utilize different variants of the celebrated Fano's inequality to derive upper bounds on the inferential power of a data reconstruction adversary when the model is trained differentially privately. Importantly, we show that if the underlying private data takes values from a set of size $M$, then the target privacy parameter $\epsilon$ can be $O(\log M)$ before the adversary gains significant inferential power. Our analysis offers theoretical evidence for the empirical effectiveness of DP against data reconstruction attacks even at relatively large values of $\epsilon$.

Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a well-known correlation-based measure of functional connectivity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.

Here, we show that the InfoNCE objective is equivalent to the ELBO in a new class of probabilistic generative model, the recognition parameterised model (RPM). When we learn the optimal prior, the RPM ELBO becomes equal to the mutual information (MI; up to a constant), establishing a connection to pre-existing self-supervised learning methods such as InfoNCE. However, practical InfoNCE methods do not use the MI as an objective; the MI is invariant to arbitrary invertible transformations, so using an MI objective can lead to highly entangled representations (Tschannen et al., 2019). Instead, the actual InfoNCE objective is a simplified lower bound on the MI which is loose even in the infinite sample limit. Thus, an objective that works (i.e. the actual InfoNCE objective) appears to be motivated as a loose bound on an objective that does not work (i.e. the true MI which gives arbitrarily entangled representations). We give an alternative motivation for the actual InfoNCE objective. In particular, we show that in the infinite sample limit, and for a particular choice of prior, the actual InfoNCE objective is equal to the ELBO (up to a constant); and the ELBO is equal to the marginal likelihood with a deterministic recognition model. Thus, we argue that our VAE perspective gives a better motivation for InfoNCE than MI, as the actual InfoNCE objective is only loosely bounded by the MI, but is equal to the ELBO/marginal likelihood (up to a constant).

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Additionally, with the remarkable success of pre-trained models in natural language processing tasks, transformer-based models like TransUNet have achieved desirable performance on multiple medical image segmentation datasets. In this paper, we conduct a survey of the most representative four medical image segmentation models in recent years. We theoretically analyze the characteristics of these models and quantitatively evaluate their performance on two benchmark datasets (i.e., Tuberculosis Chest X-rays and ovarian tumors). Finally, we discuss the main challenges and future trends in medical image segmentation. Our work can assist researchers in the related field to quickly establish medical segmentation models tailored to specific regions.  ( 2 min )

Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and high-performing solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted on a limited set of domains - mainly applied to locomotion, where the fitness and the behavior signal are dense. Grasping is a crucial task for manipulation in robotics. Despite the efforts of many research communities, this task is yet to be solved. Grasping cumulates unprecedented challenges in QD literature: it suffers from reward sparsity, behavioral sparsity, and behavior space misalignment. The present work studies how QD can address grasping. Experiments have been conducted on 15 different methods on 10 grasping domains, corresponding to 2 different robot-gripper setups and 5 standard objects. An evaluation framework that distinguishes the evaluation of an algorithm from its internal components has also been proposed for a fair comparison. The obtained results show that MAP-Elites variants that select successful solutions in priority outperform all the compared methods on the studied metrics by a large margin. We also found experimental evidence that sparse interaction can lead to deceptive novelty. To our knowledge, the ability to efficiently produce examples of grasping trajectories demonstrated in this work has no precedent in the literature.  ( 2 min )

Gradient-based optimization is now ubiquitous across graphics, but unfortunately can not be applied to problems with undefined or zero gradients. To circumvent this issue, the loss function can be manually replaced by a "surrogate" that has similar minima but is differentiable. Our proposed framework, ZeroGrads, automates this process by learning a neural approximation of the objective function, the surrogate, which in turn can be used to differentiate through arbitrary black-box graphics pipelines. We train the surrogate on an actively smoothed version of the objective and encourage locality, focusing the surrogate's capacity on what matters at the current training episode. The fitting is performed online, alongside the parameter optimization, and self-supervised, without pre-computed data or pre-trained models. As sampling the objective is expensive (it requires a full rendering or simulator run), we devise an efficient sampling scheme that allows for tractable run-times and competitive performance at little overhead. We demonstrate optimizing diverse non-convex, non-differentiable black-box problems in graphics, such as visibility in rendering, discrete parameter spaces in procedural modelling or optimal control in physics-driven animation. In contrast to more traditional algorithms, our approach scales well to higher dimensions, which we demonstrate on problems with up to 35k interlinked variables.

Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multiple trainable layers in tandem with these physical systems poses challenges, as they are difficult to fully characterize and describe with differentiable functions, hindering the use of error backpropagation algorithm. The recently introduced Forward-Forward Algorithm (FFA) eliminates the need for perfect characterization of the learning system and shows promise for efficient training with large numbers of programmable parameters. The FFA does not require backpropagating an error signal to update the weights, rather the weights are updated by only sending information in one direction. The local loss function for each set of trainable weights enables low-power analog hardware implementations without resorting to metaheuristic algorithms or reinforcement learning. In this paper, we present an experiment utilizing multimode nonlinear wave propagation in an optical fiber demonstrating the feasibility of the FFA approach using an optical system. The results show that incorporating optical transforms in multilayer NN architectures trained with the FFA, can lead to performance improvements, even with a relatively small number of trainable weights. The proposed method offers a new path to the challenge of training optical NNs and provides insights into leveraging physical transformations for enhancing NN performance.

We present a robust deep incremental learning framework for regression tasks on financial temporal tabular datasets which is built upon the incremental use of commonly available tabular and time series prediction models to adapt to distributional shifts typical of financial datasets. The framework uses a simple basic building block (decision trees) to build self-similar models of any required complexity to deliver robust performance under adverse situations such as regime changes, fat-tailed distributions, and low signal-to-noise ratios. As a detailed study, we demonstrate our scheme using XGBoost models trained on the Numerai dataset and show that a two layer deep ensemble of XGBoost models over different model snapshots delivers high quality predictions under different market regimes. We also show that the performance of XGBoost models with different number of boosting rounds in three scenarios (small, standard and large) is monotonically increasing with respect to model size and converges towards the generalisation upper bound. We also evaluate the robustness of the model under variability of different hyperparameters, such as model complexity and data sampling settings. Our model has low hardware requirements as no specialised neural architectures are used and each base model can be independently trained in parallel.

It is desirable for statistical models to detect signals of interest independently of their position. If the data is generated by some smooth process, this additional structure should be taken into account. We introduce a new class of neural networks that are shift invariant and preserve smoothness of the data: functional neural networks (FNNs). For this, we use methods from functional data analysis (FDA) to extend multi-layer perceptrons and convolutional neural networks to functional data. We propose different model architectures, show that the models outperform a benchmark model from FDA in terms of accuracy and successfully use FNNs to classify electroencephalography (EEG) data.

We propose an efficient distributed out-of-memory implementation of the Non-negative Matrix Factorization (NMF) algorithm for heterogeneous high-performance-computing (HPC) systems. The proposed implementation is based on prior work on NMFk, which can perform automatic model selection and extract latent variables and patterns from data. In this work, we extend NMFk by adding support for dense and sparse matrix operation on multi-node, multi-GPU systems. The resulting algorithm is optimized for out-of-memory (OOM) problems where the memory required to factorize a given matrix is greater than the available GPU memory. Memory complexity is reduced by batching/tiling strategies, and sparse and dense matrix operations are significantly accelerated with GPU cores (or tensor cores when available). Input/Output (I/O) latency associated with batch copies between host and device is hidden using CUDA streams to overlap data transfers and compute asynchronously, and latency associated with collective communications (both intra-node and inter-node) is reduced using optimized NVIDIA Collective Communication Library NCCL based communicators. Benchmark results show significant improvement, from 32X to 76x speedup, with the new implementation using GPUs over the CPU-based NMFk. Good weak scaling was demonstrated on up to 4096 multi-GPU cluster nodes with approximately 25,000 GPUs when decomposing a dense 340 Terabyte-size matrix and an 11 Exabyte-size sparse matrix of density 10e-6.

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training. But in real-life, models are often applied on data with new classes, which can lead to massive misclassification and thus significantly degrade performance. Hence, developing open-set classification methods is crucial to determine if a given sample belongs to a known class. Existing methods for open-set node classification generally use transductive learning with part or all of the features of real unseen class nodes to help with open-set classification. In this paper, we propose a novel generative open-set node classification method, i.e. $\mathcal{G}^2Pxy$, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and external unknown proxies are generated via mixup to efficiently anticipate the distribution of novel classes. Using the generated proxies, a closed-set classifier can be transformed into an open-set one, by augmenting it with an extra proxy classifier. Under the constraints of both cross entropy loss and complement entropy loss, $\mathcal{G}^2Pxy$ achieves superior effectiveness for unknown class detection and known class classification, which is validated by experiments on benchmark graph datasets. Moreover, $\mathcal{G}^2Pxy$ does not have specific requirement on the GNN architecture and shows good generalizations.

Automated medical report generation has become increasingly important in medical analysis. It can produce computer-aided diagnosis descriptions and thus significantly alleviate the doctors' work. Inspired by the huge success of neural machine translation and image captioning, various deep learning methods have been proposed for medical report generation. However, due to the inherent properties of medical data, including data imbalance and the length and correlation between report sequences, the generated reports by existing methods may exhibit linguistic fluency but lack adequate clinical accuracy. In this work, we propose an image-to-indicator hierarchical transformer (IIHT) framework for medical report generation. It consists of three modules, i.e., a classifier module, an indicator expansion module and a generator module. The classifier module first extracts image features from the input medical images and produces disease-related indicators with their corresponding states. The disease-related indicators are subsequently utilised as input for the indicator expansion module, incorporating the "data-text-data" strategy. The transformer-based generator then leverages these extracted features along with image features as auxiliary information to generate final reports. Furthermore, the proposed IIHT method is feasible for radiologists to modify disease indicators in real-world scenarios and integrate the operations into the indicator expansion module for fluent and accurate medical report generation. Extensive experiments and comparisons with state-of-the-art methods under various evaluation metrics demonstrate the great performance of the proposed method.

In this short note, I show how to adapt to H\"{o}lder smoothness using normalized gradients in a black-box way. Moreover, the bound will depend on a novel notion of local H\"{o}lder smoothness. The main idea directly comes from Levy [2017].

Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications.

The ability to cope accurately and fast with Out-Of-Distribution (OOD) samples is crucial in real-world safety demanding applications. In this work we first study the interplay between critical points of 3D point clouds and OOD samples. Our findings are that common corruptions and outliers are often interpreted as critical points. We generalize the notion of critical points into importance measures. We show that training a classification network based only on less important points dramatically improves robustness, at a cost of minor performance loss on the clean set. We observe that normalized entropy is highly informative for corruption analysis. An adaptive threshold based on normalized entropy is suggested for selecting the set of uncritical points. Our proposed importance measure is extremely fast to compute. We show it can be used for a variety of applications, such as Explainable AI (XAI), Outlier Removal, Uncertainty Estimation, Robust Classification and Adversarial Defense. We reach SOTA results on the two latter tasks.

We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon-TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon-TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on 29 benchmark datasets, AutoGluon-TimeSeries demonstrates strong empirical performance, outperforming a range of forecasting methods in terms of both point and quantile forecast accuracy, and often even improving upon the best-in-hindsight combination of prior methods.

Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were applied in practice.The field of Medical AI faces various challenges, in terms of building user trust, complying with regulations, using data ethically.Explainable AI (XAI) aims to enable humans understand AI and trust its results. This paper presents a literature review on the recent developments of XAI solutions for medical decision support, based on a representative sample of 198 articles published in recent years. The systematic synthesis of the relevant articles resulted in several findings. (1) model-agnostic XAI techniques were mostly employed in these solutions, (2) deep learning models are utilized more than other types of machine learning models, (3) explainability was applied to promote trust, but very few works reported the physicians participation in the loop, (4) visual and interactive user interface is more useful in understanding the explanation and the recommendation of the system. More research is needed in collaboration between medical and AI experts, that could guide the development of suitable frameworks for the design, implementation, and evaluation of XAI solutions in medicine.

With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitude, and noise of RS data. The typical approach for merging multiple RS sources has been input-level fusion, but other - more advanced - fusion strategies may outperform this traditional approach. This work assesses different fusion strategies for crop classification in the CropHarvest dataset. The fusion methods proposed in this work outperform models based on individual views and previous fusion methods. We do not find one single fusion method that consistently outperforms all other approaches. Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance. Despite this, we suggest a preliminary criterion for the selection of fusion methods.

Deceptive text classification is a critical task in natural language processing that aims to identify deceptive or fraudulent content. This study presents a comparative analysis of machine learning and transformer-based approaches for deceptive text classification. We investigate the effectiveness of traditional machine learning algorithms and state-of-the-art transformer models, such as BERT, XLNET, DistilBERT, and RoBERTa, in detecting deceptive text. A labeled dataset consisting of deceptive and non-deceptive texts is used for training and evaluation purposes. Through extensive experimentation, we compare the performance metrics, including accuracy, precision, recall, and F1 score, of the different approaches. The results of this study shed light on the strengths and limitations of machine learning and transformer-based methods for deceptive text classification, enabling researchers and practitioners to make informed decisions when dealing with deceptive content

Deep learning classifiers achieve state-of-the-art performance in various risk detection applications. They explore rich semantic representations and are supposed to automatically discover risk behaviors. However, due to the lack of transparency, the behavioral semantics cannot be conveyed to downstream security experts to reduce their heavy workload in security analysis. Although feature attribution (FA) methods can be used to explain deep learning, the underlying classifier is still blind to what behavior is suspicious, and the generated explanation cannot adapt to downstream tasks, incurring poor explanation fidelity and intelligibility. In this paper, we propose FINER, the first framework for risk detection classifiers to generate high-fidelity and high-intelligibility explanations. The high-level idea is to gather explanation efforts from model developer, FA designer, and security experts. To improve fidelity, we fine-tune the classifier with an explanation-guided multi-task learning strategy. To improve intelligibility, we engage task knowledge to adjust and ensemble FA methods. Extensive evaluations show that FINER improves explanation quality for risk detection. Moreover, we demonstrate that FINER outperforms a state-of-the-art tool in facilitating malware analysis.

We propose CONF-TSASR, a non-autoregressive end-to-end time-frequency domain architecture for single-channel target-speaker automatic speech recognition (TS-ASR). The model consists of a TitaNet based speaker embedding module, a Conformer based masking as well as ASR modules. These modules are jointly optimized to transcribe a target-speaker, while ignoring speech from other speakers. For training we use Connectionist Temporal Classification (CTC) loss and introduce a scale-invariant spectrogram reconstruction loss to encourage the model better separate the target-speaker's spectrogram from mixture. We obtain state-of-the-art target-speaker word error rate (TS-WER) on WSJ0-2mix-extr (4.2%). Further, we report for the first time TS-WER on WSJ0-3mix-extr (12.4%), LibriSpeech2Mix (4.2%) and LibriSpeech3Mix (7.6%) datasets, establishing new benchmarks for TS-ASR. The proposed model will be open-sourced through NVIDIA NeMo toolkit.

Environmental perception is a key element of autonomous driving because the information received from the perception module influences core driving decisions. An outstanding challenge in real-time perception for autonomous driving lies in finding the best trade-off between detection quality and latency. Major constraints on both computation and power have to be taken into account for real-time perception in autonomous vehicles. Larger object detection models tend to produce the best results, but are also slower at runtime. Since the most accurate detectors cannot run in real-time locally, we investigate the possibility of offloading computation to edge and cloud platforms, which are less resource-constrained. We create a synthetic dataset to train object detection models and evaluate different offloading strategies. Using real hardware and network simulations, we compare different trade-offs between prediction quality and end-to-end delay. Since sending raw frames over the network implies additional transmission delays, we also explore the use of JPEG and H.265 compression at varying qualities and measure their impact on prediction metrics. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.

This paper introduces a novel deep-learning based generator of synthetic graphs that represent intra-Autonomous System (AS) in the Internet, named Deep-generative graphs for the Internet (DGGI). It also presents a novel massive dataset of real intra-AS graphs extracted from the project Internet Topology Data Kit (ITDK), called Internet Graphs (IGraphs). To create IGraphs, the Filtered Recurrent Multi-level (FRM) algorithm for community extraction was developed. It is shown that DGGI creates synthetic graphs which accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the Maximum Mean Discrepancy (MMD) metric 84.4%, 95.1%, 97.9%, and 94.7% for assortativity, betweenness, clustering, and node degree, respectively.

Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades. Recent breakthroughs like AlphaFold2 that use transformers to attend directly over large quantities of raw MSAs have reaffirmed their importance. Generation of MSAs is highly computationally intensive, however, and no datasets comparable to those used to train AlphaFold2 have been made available to the research community, hindering progress in machine learning for proteins. To remedy this problem, we introduce OpenProteinSet, an open-source corpus of more than 16 million MSAs, associated structural homologs from the Protein Data Bank, and AlphaFold2 protein structure predictions. We have previously demonstrated the utility of OpenProteinSet by successfully retraining AlphaFold2 on it. We expect OpenProteinSet to be broadly useful as training and validation data for 1) diverse tasks focused on protein structure, function, and design and 2) large-scale multimodal machine learning research.

Efficient and timely calculations of Machine Learning (ML) algorithms are essential for emerging technologies like autonomous driving, the Internet of Things (IoT), and edge computing. One of the primary ML algorithms used in such systems is Convolutional Neural Networks (CNNs), which demand high computational resources. This requirement has led to the use of ML accelerators like GPGPUs to meet design constraints. However, selecting the most suitable accelerator involves Design Space Exploration (DSE), a process that is usually time-consuming and requires significant manual effort. Our work presents approaches to expedite the DSE process by identifying the most appropriate GPGPU for CNN inferencing systems. We have developed a quick and precise technique for forecasting the power and performance of CNNs during inference, with a MAPE of 5.03% and 5.94%, respectively. Our approach empowers computer architects to estimate power and performance in the early stages of development, reducing the necessity for numerous prototypes. This saves time and money while also improving the time-to-market period.

This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics. Existing implementations adapt classic isosurface extraction algorithms like Marching Cubes or Dual Contouring; these techniques were designed to extract meshes from fixed, known fields, and in the optimization setting they lack the degrees of freedom to represent high-quality feature-preserving meshes, or suffer from numerical instabilities. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives. Our main insight is to introduce additional carefully-chosen parameters into the representation, which allow local flexible adjustments to the extracted mesh geometry and connectivity. These parameters are updated along with the underlying scalar field via automatic differentiation when optimizing for a downstream task. We base our extraction scheme on Dual Marching Cubes for improved topological properties, and present extensions to optionally generate tetrahedral and hierarchically-adaptive meshes. Extensive experiments validate FlexiCubes on both synthetic benchmarks and real-world applications, showing that it offers significant improvements in mesh quality and geometric fidelity.

Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and energy efficiency. However, to reach the full capacity of an optical neural network it is necessary that the computing not only for the inference, but also for the training be implemented optically. The primary algorithm for training a neural network is backpropagation, in which the calculation is performed in the order opposite to the information flow for inference. While straightforward in a digital computer, optical implementation of backpropagation has so far remained elusive, particularly because of the conflicting requirements for the optical element that implements the nonlinear activation function. In this work, we address this challenge for the first time with a surprisingly simple and generic scheme. Saturable absorbers are employed for the role of the activation units, and the required properties are achieved through a pump-probe process, in which the forward propagating signal acts as the pump and backward as the probe. Our approach is adaptable to various analog platforms, materials, and network structures, and it demonstrates the possibility of constructing neural networks entirely reliant on analog optical processes for both training and inference tasks.

Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the stakeholders with data science and Machine Learning (ML) related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. Therefore, they failed to address the architecture viewpoints and views responsive to the concerns of the data science community. In this paper, we address this gap by establishing the architecture frameworks adapted to meet the requirements of modern applications and organizations where ML artifacts are both prevalent and crucial. In particular, we focus on ML-enabled Cyber-Physical Systems (CPSs) and propose two sets of merit criteria for their efficient development and performance assessment, namely the criteria for evaluating and benchmarking ML-enabled CPSs, and the criteria for evaluation and benchmarking of the tools intended to support users through the modeling and development pipeline. In this study, we deploy multiple empirical and qualitative research methods based on literature review and survey instruments including expert interviews and an online questionnaire. We collect, analyze, and integrate the opinions of 77 experts from more than 25 organizations in over 10 countries to devise and validate the proposed framework.  ( 2 min )

Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existing GNN-based graph clustering methods, they often overlook the quality of graph structure, which is inherent in real-world graphs due to their sparse and multifarious nature, leading to subpar performance. Graph structure learning allows refining the input graph by adding missing links and removing spurious connections. However, previous endeavors in graph structure learning have predominantly centered around supervised settings, and cannot be directly applied to our specific clustering tasks due to the absence of ground-truth labels. To bridge the gap, we propose a novel method called \textbf{ho}mophily-enhanced structure \textbf{le}arning for graph clustering (HoLe). Our motivation stems from the observation that subtly enhancing the degree of homophily within the graph structure can significantly improve GNNs and clustering outcomes. To realize this objective, we develop two clustering-oriented structure learning modules, i.e., hierarchical correlation estimation and cluster-aware sparsification. The former module enables a more accurate estimation of pairwise node relationships by leveraging guidance from latent and clustering spaces, while the latter one generates a sparsified structure based on the similarity matrix and clustering assignments. Additionally, we devise a joint optimization approach alternating between training the homophily-enhanced structure learning and GNN-based clustering, thereby enforcing their reciprocal effects. Extensive experiments on seven benchmark datasets of various types and scales, across a range of clustering metrics, demonstrate the superiority of HoLe against state-of-the-art baselines.  ( 3 min )

Recently, methods for skeleton-based human activity recognition have been shown to be vulnerable to adversarial attacks. However, these attack methods require either the full knowledge of the victim (i.e. white-box attacks), access to training data (i.e. transfer-based attacks) or frequent model queries (i.e. black-box attacks). All their requirements are highly restrictive, raising the question of how detrimental the vulnerability is. In this paper, we show that the vulnerability indeed exists. To this end, we consider a new attack task: the attacker has no access to the victim model or the training data or labels, where we coin the term hard no-box attack. Specifically, we first learn a motion manifold where we define an adversarial loss to compute a new gradient for the attack, named skeleton-motion-informed (SMI) gradient. Our gradient contains information of the motion dynamics, which is different from existing gradient-based attack methods that compute the loss gradient assuming each dimension in the data is independent. The SMI gradient can augment many gradient-based attack methods, leading to a new family of no-box attack methods. Extensive evaluation and comparison show that our method imposes a real threat to existing classifiers. They also show that the SMI gradient improves the transferability and imperceptibility of adversarial samples in both no-box and transfer-based black-box settings.  ( 2 min )

Automated driving has the potential to revolutionize personal, public, and freight mobility. Besides the enormous challenge of perception, i.e. accurately perceiving the environment using available sensor data, automated driving comprises planning a safe, comfortable, and efficient motion trajectory. To promote safety and progress, many works rely on modules that predict the future motion of surrounding traffic. Modular automated driving systems commonly handle prediction and planning as sequential separate tasks. While this accounts for the influence of surrounding traffic on the ego-vehicle, it fails to anticipate the reactions of traffic participants to the ego-vehicle's behavior. Recent works suggest that integrating prediction and planning in an interdependent joint step is necessary to achieve safe, efficient, and comfortable driving. While various models implement such integrated systems, a comprehensive overview and theoretical understanding of different principles are lacking. We systematically review state-of-the-art deep learning-based prediction, planning, and integrated prediction and planning models. Different facets of the integration ranging from model architecture and model design to behavioral aspects are considered and related to each other. Moreover, we discuss the implications, strengths, and limitations of different integration methods. By pointing out research gaps, describing relevant future challenges, and highlighting trends in the research field, we identify promising directions for future research.

Recent research in language-guided visual navigation has demonstrated a significant demand for the diversity of traversable environments and the quantity of supervision for training generalizable agents. To tackle the common data scarcity issue in existing vision-and-language navigation datasets, we propose an effective paradigm for generating large-scale data for learning, which applies 1200+ photo-realistic environments from HM3D and Gibson datasets and synthesizes 4.9 million instruction trajectory pairs using fully-accessible resources on the web. Importantly, we investigate the influence of each component in this paradigm on the agent's performance and study how to adequately apply the augmented data to pre-train and fine-tune an agent. Thanks to our large-scale dataset, the performance of an existing agent can be pushed up (+11% absolute with regard to previous SoTA) to a significantly new best of 80% single-run success rate on the R2R test split by simple imitation learning. The long-lasting generalization gap between navigating in seen and unseen environments is also reduced to less than 1% (versus 8% in the previous best method). Moreover, our paradigm also facilitates different models to achieve new state-of-the-art navigation results on CVDN, REVERIE, and R2R in continuous environments.

This PhD. Thesis concerns the study and development of hierarchical representations for spatio-temporal visual attention modeling and understanding in video sequences. More specifically, we propose two computational models for visual attention. First, we present a generative probabilistic model for context-aware visual attention modeling and understanding. Secondly, we develop a deep network architecture for visual attention modeling, which first estimates top-down spatio-temporal visual attention, and ultimately serves for modeling attention in the temporal domain.  ( 2 min )

Retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the goal to provide a synthesis route. Its two primary research directions, single-step retrosynthesis prediction, which models the chemical reaction logic, and multi-step synthesis planning, which tries to find the correct sequence of reactions, are inherently intertwined. Still, this connection is not reflected in contemporary research. In this work, we combine these two major research directions by applying multiple single-step retrosynthesis models within multi-step synthesis planning and analyzing their impact using public and proprietary reaction data. We find a disconnection between high single-step performance and potential route-finding success, suggesting that single-step models must be evaluated within synthesis planning in the future. Furthermore, we show that the commonly used single-step retrosynthesis benchmark dataset USPTO-50k is insufficient as this evaluation task does not represent model performance and scalability on larger and more diverse datasets. For multi-step synthesis planning, we show that the choice of the single-step model can improve the overall success rate of synthesis planning by up to +28% compared to the commonly used baseline model. Finally, we show that each single-step model finds unique synthesis routes, and differs in aspects such as route-finding success, the number of found synthesis routes, and chemical validity, making the combination of single-step retrosynthesis prediction and multi-step synthesis planning a crucial aspect when developing future methods.  ( 3 min )

The connection between Residual Neural Networks (ResNets) and continuous-time control systems (known as NeurODEs) has led to a mathematical analysis of neural networks which has provided interesting results of both theoretical and practical significance. However, by construction, NeurODEs have been limited to describing constant-width layers, making them unsuitable for modeling deep learning architectures with layers of variable width. In this paper, we propose a continuous-time Autoencoder, which we call AutoencODE, based on a modification of the controlled field that drives the dynamics. This adaptation enables the extension of the mean-field control framework originally devised for conventional NeurODEs. In this setting, we tackle the case of low Tikhonov regularization, resulting in potentially non-convex cost landscapes. While the global results obtained for high Tikhonov regularization may not hold globally, we show that many of them can be recovered in regions where the loss function is locally convex. Inspired by our theoretical findings, we develop a training method tailored to this specific type of Autoencoders with residual connections, and we validate our approach through numerical experiments conducted on various examples.  ( 2 min )

Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerable to adversarial examples, threatening their practical deployment. Despite the many research endeavors have been made to tackle this issue in recent years, the diversity of adversarial examples on 3D point clouds makes them more challenging to defend against than those on 2D images. For examples, attackers can generate adversarial examples by adding, shifting, or removing points. Consequently, existing defense strategies are hard to counter unseen point cloud adversarial examples. In this paper, we first establish a comprehensive, and rigorous point cloud adversarial robustness benchmark to evaluate adversarial robustness, which can provide a detailed understanding of the effects of the defense and attack methods. We then collect existing defense tricks in point cloud adversarial defenses and then perform extensive and systematic experiments to identify an effective combination of these tricks. Furthermore, we propose a hybrid training augmentation methods that consider various types of point cloud adversarial examples to adversarial training, significantly improving the adversarial robustness. By combining these tricks, we construct a more robust defense framework achieving an average accuracy of 83.45\% against various attacks, demonstrating its capability to enabling robust learners. Our codebase are open-sourced on: \url{https://github.com/qiufan319/benchmark_pc_attack.git}.  ( 3 min )

The task of retrieving already debunked narratives aims to detect stories that have already been fact-checked. The successful detection of claims that have already been debunked not only reduces the manual efforts of professional fact-checkers but can also contribute to slowing the spread of misinformation. Mainly due to the lack of readily available data, this is an understudied problem, particularly when considering the cross-lingual task, i.e. the retrieval of fact-checking articles in a language different from the language of the online post being checked. This paper fills this gap by (i) creating a novel dataset to enable research on cross-lingual retrieval of already debunked narratives, using tweets as queries to a database of fact-checking articles; (ii) presenting an extensive experiment to benchmark fine-tuned and off-the-shelf multilingual pre-trained Transformer models for this task; and (iii) proposing a novel multistage framework that divides this cross-lingual debunk retrieval task into refinement and re-ranking stages. Results show that the task of cross-lingual retrieval of already debunked narratives is challenging and off-the-shelf Transformer models fail to outperform a strong lexical-based baseline (BM25). Nevertheless, our multistage retrieval framework is robust, outperforming BM25 in most scenarios and enabling cross-domain and zero-shot learning, without significantly harming the model's performance.  ( 2 min )

Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions. In this work, we propose SegMatch, a semi supervised learning method to reduce the need for expensive annotation for laparoscopic and robotic surgical images. SegMatch builds on FixMatch, a widespread semi supervised classification pipeline combining consistency regularization and pseudo labelling, and adapts it for the purpose of segmentation. In our proposed SegMatch, the unlabelled images are weakly augmented and fed into the segmentation model to generate a pseudo-label to enforce the unsupervised loss against the output of the model for the adversarial augmented image on the pixels with a high confidence score. Our adaptation for segmentation tasks includes carefully considering the equivariance and invariance properties of the augmentation functions we rely on. To increase the relevance of our augmentations, we depart from using only handcrafted augmentations and introduce a trainable adversarial augmentation strategy. Our algorithm was evaluated on the MICCAI Instrument Segmentation Challenge datasets Robust-MIS 2019 and EndoVis 2017. Our results demonstrate that adding unlabelled data for training purposes allows us to surpass the performance of fully supervised approaches which are limited by the availability of training data in these challenges. SegMatch also outperforms a range of state-of-the-art semi-supervised learning semantic segmentation models in different labelled to unlabelled data ratios.  ( 2 min )

In this paper, the state of the art in the field of pedestrian trajectory prediction is evaluated alongside the constant velocity model (CVM) with respect to its applicability in autonomous vehicles. The evaluation is conducted on the widely-used ETH/UCY dataset where the Average Displacement Error (ADE) and the Final Displacement Error (FDE) are reported. To align with requirements in real-world applications, modifications are made to the input features of the initially proposed models. An ablation study is conducted to examine the influence of the observed motion history on the prediction performance, thereby establishing a better understanding of its impact. Additionally, the inference time of each model is measured to evaluate the scalability of each model when confronted with varying amounts of agents. The results demonstrate that simple models remain competitive when generating single trajectories, and certain features commonly thought of as useful have little impact on the overall performance across different architectures. Based on these findings, recommendations are proposed to guide the future development of trajectory prediction algorithms.  ( 2 min )

Large skew-t factor copula models are attractive for the modeling of financial data because they allow for asymmetric and extreme tail dependence. We show that the copula implicit in the skew-t distribution of Azzalini and Capitanio (2003) allows for a higher level of pairwise asymmetric dependence than two popular alternative skew-t copulas. Estimation of this copula in high dimensions is challenging, and we propose a fast and accurate Bayesian variational inference (VI) approach to do so. The method uses a conditionally Gaussian generative representation of the skew-t distribution to define an augmented posterior that can be approximated accurately. A fast stochastic gradient ascent algorithm is used to solve the variational optimization. The new methodology is used to estimate copula models for intraday returns from 2017 to 2021 on 93 U.S. equities. The copula captures substantial heterogeneity in asymmetric dependence over equity pairs, in addition to the variability in pairwise correlations. We show that intraday predictive densities from the skew-t copula are more accurate than from some other copula models, while portfolio selection strategies based on the estimated pairwise tail dependencies improve performance relative to the benchmark index.  ( 2 min )

In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model. The main challenge in this setting is balancing privacy with both classification accuracy of the learnt model as well as the number of bits communicated between the clients and server. Prior work has achieved a good trade-off by designing a privacy-aware compression mechanism, called the minimum variance unbiased (MVU) mechanism, that numerically solves an optimization problem to determine the parameters of the mechanism. This paper builds upon it by introducing a new interpolation procedure in the numerical design process that allows for a far more efficient privacy analysis. The result is the new Interpolated MVU mechanism that is more scalable, has a better privacy-utility trade-off, and provides SOTA results on communication-efficient private FL on a variety of datasets.  ( 2 min )

As data shift or new data become available, updating clinical machine learning models may be necessary to maintain or improve performance over time. However, updating a model can introduce compatibility issues when the behavior of the updated model does not align with user expectations, resulting in poor user-model team performance. Existing compatibility measures depend on model decision thresholds, limiting their applicability in settings where models are used to generate rankings based on estimated risk. To address this limitation, we propose a novel rank-based compatibility measure, $C^R$, and a new loss function that aims to optimize discriminative performance while encouraging good compatibility. Applied to a case study in mortality risk stratification leveraging data from MIMIC, our approach yields more compatible models while maintaining discriminative performance compared to existing model selection techniques, with an increase in $C^R$ of $0.019$ ($95\%$ confidence interval: $0.005$, $0.035$). This work provides new tools to analyze and update risk stratification models used in clinical care.  ( 2 min )

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous applications. However, its practicality in addressing various real-world scenarios, characterized by diverse and unpredictable dynamics, noisy signals, and large state and action spaces, remains limited. This limitation stems from issues such as poor data efficiency, limited generalization capabilities, a lack of safety guarantees, and the absence of interpretability, among other factors. To overcome these challenges and improve performance across these crucial metrics, one promising avenue is to incorporate additional structural information about the problem into the RL learning process. Various sub-fields of RL have proposed methods for incorporating such inductive biases. We amalgamate these diverse methodologies under a unified framework, shedding light on the role of structure in the learning problem, and classify these methods into distinct patterns of incorporating structure. By leveraging this comprehensive framework, we provide valuable insights into the challenges of structured RL and lay the groundwork for a design pattern perspective on RL research. This novel perspective paves the way for future advancements and aids in developing more effective and efficient RL algorithms that can potentially handle real-world scenarios better.  ( 2 min )

Deep learning training training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deeper and deeper layers are proposed with huge success with resnet structures having around 152 layers. Shallow convolution neural networks(CNN's) are still an active research, where some phenomena are still unexplained. Activation functions used in the network are of utmost importance, as they provide non linearity to the networks. Relu's are the most commonly used activation function.We show a complex piece-wise linear(PWL) activation in the hidden layer. We show that these PWL activations work much better than relu activations in our networks for convolution neural networks and multilayer perceptrons. Result comparison in PyTorch for shallow and deep CNNs are given to further strengthen our case.

Recent work has shown that it is possible to resynthesize high-quality speech based, not on text, but on low bitrate discrete units that have been learned in a self-supervised fashion and can therefore capture expressive aspects of speech that are hard to transcribe (prosody, voice styles, non-verbal vocalization). The adoption of these methods is still limited by the fact that most speech synthesis datasets are read, severely limiting spontaneity and expressivity. Here, we introduce Expresso, a high-quality expressive speech dataset for textless speech synthesis that includes both read speech and improvised dialogues rendered in 26 spontaneous expressive styles. We illustrate the challenges and potentials of this dataset with an expressive resynthesis benchmark where the task is to encode the input in low-bitrate units and resynthesize it in a target voice while preserving content and style. We evaluate resynthesis quality with automatic metrics for different self-supervised discrete encoders, and explore tradeoffs between quality, bitrate and invariance to speaker and style. All the dataset, evaluation metrics and baseline models are open source

The decentralized and privacy-preserving nature of federated learning (FL) makes it vulnerable to backdoor attacks aiming to manipulate the behavior of the resulting model on specific adversary-chosen inputs. However, most existing defenses based on statistical differences take effect only against specific attacks, especially when the malicious gradients are similar to benign ones or the data are highly non-independent and identically distributed (non-IID). In this paper, we revisit the distance-based defense methods and discover that i) Euclidean distance becomes meaningless in high dimensions and ii) malicious gradients with diverse characteristics cannot be identified by a single metric. To this end, we present a simple yet effective defense strategy with multi-metrics and dynamic weighting to identify backdoors adaptively. Furthermore, our novel defense has no reliance on predefined assumptions over attack settings or data distributions and little impact on benign performance. To evaluate the effectiveness of our approach, we conduct comprehensive experiments on different datasets under various attack settings, where our method achieves the best defensive performance. For instance, we achieve the lowest backdoor accuracy of 3.06% under the difficult Edge-case PGD, showing significant superiority over previous defenses. The results also demonstrate that our method can be well-adapted to a wide range of non-IID degrees without sacrificing the benign performance.

In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and edges. The existing methods have achieved good performance by transferring generalized knowledge extracted from rich-labeled classes in source HG(s) to few-labeled classes in a target HG. However, these methods only consider the single-heterogeneity scenario where the source and target HGs share a fixed set of node/edge types, ignoring the more general scenario of cross-heterogeneity, where each HG can have a different and non-fixed set of node/edge types. To this end, we focus on the unexplored cross-heterogeneity scenario and propose a novel model for Cross-heterogeneity Graph Few-shot Learning, namely CGFL. In CGFL, we first extract meta-patterns to capture heterogeneous information and propose a multi-view heterogeneous graph neural network (MHGN) to learn meta-patterns across HGs. Then, we propose a score module to measure the informativeness of labeled samples and determine the transferability of each source HG. Finally, by integrating MHGN and the score module into a meta-learning mechanism, CGFL can effectively transfer generalized knowledge to predict new classes with few-labeled data. Extensive experiments on four real-world datasets have demonstrated the superior performance of CGFL over the state-of-the-art methods.

This paper studies Byzantine-robust stochastic optimization over a decentralized network, where every agent periodically communicates with its neighbors to exchange local models, and then updates its own local model by stochastic gradient descent (SGD). The performance of such a method is affected by an unknown number of Byzantine agents, which conduct adversarially during the optimization process. To the best of our knowledge, there is no existing work that simultaneously achieves a linear convergence speed and a small learning error. We observe that the learning error is largely dependent on the intrinsic stochastic gradient noise. Motivated by this observation, we introduce two variance reduction methods, stochastic average gradient algorithm (SAGA) and loopless stochastic variance-reduced gradient (LSVRG), to Byzantine-robust decentralized stochastic optimization for eliminating the negative effect of the stochastic gradient noise. The two resulting methods, BRAVO-SAGA and BRAVO-LSVRG, enjoy both linear convergence speeds and stochastic gradient noise-independent learning errors. Such learning errors are optimal for a class of methods based on total variation (TV)-norm regularization and stochastic subgradient update. We conduct extensive numerical experiments to demonstrate their effectiveness under various Byzantine attacks.

Hypernetworks, or hypernets in short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression etc. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning etc. Despite their success across different problem settings, currently, there is no review available to inform the researchers about the developments and to help in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example to train deep neural networks using hypernets and propose categorizing hypernets based on five design criteria as inputs, outputs, variability of inputs and outputs, and architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain under-explored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks.

Causal inference is a crucial goal of science, enabling researchers to arrive at meaningful conclusions regarding the predictions of hypothetical interventions using observational data. Path models, Structural Equation Models (SEMs), and, more generally, Directed Acyclic Graphs (DAGs), provide a means to unambiguously specify assumptions regarding the causal structure underlying a phenomenon. Unlike DAGs, which make very few assumptions about the functional and parametric form, SEM assumes linearity. This can result in functional misspecification which prevents researchers from undertaking reliable effect size estimation. In contrast, we propose Super Learner Equation Modeling, a path modeling technique integrating machine learning Super Learner ensembles. We empirically demonstrate its ability to provide consistent and unbiased estimates of causal effects, its competitive performance for linear models when compared with SEM, and highlight its superiority over SEM when dealing with non-linear relationships. We provide open-source code, and a tutorial notebook with example usage, accentuating the easy-to-use nature of the method.

The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e.g., 3D meshes, over the Internet. Detailed high-resolution assets can pose a challenge to storage as well as transmission bandwidth, and level-of-detail techniques are often used to transmit an asset using an appropriate bandwidth budget. It is especially desirable for these methods to transmit data progressively, improving the quality of the geometry with more data. Our key insight is that the geometric details of 3D meshes often exhibit similar local patterns even across different shapes, and thus can be effectively represented with a shared learned generative space. We learn this space using a subdivision-based encoder-decoder architecture trained in advance on a large collection of surfaces. We further observe that additional residual features can be transmitted progressively between intermediate levels of subdivision that enable the client to control the tradeoff between bandwidth cost and quality of reconstruction, providing a neural progressive mesh representation. We evaluate our method on a diverse set of complex 3D shapes and demonstrate that it outperforms baselines in terms of compression ratio and reconstruction quality.

This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function evaluations of a genetic algorithm (GA) to approximate solutions for NDPs. Using three test networks, two NDP variants and an exact solver as benchmark, we show that on average, our proposed framework can provide solutions within 1.5% gap of the best results in less than 0.5% of the time used by the exact solution procedure. Our framework can be utilized within an expert system for infrastructure planning to determine the best infrastructure planning and management decisions under different scenarios. Given the flexibility of the framework, it can easily be adapted to many other decision problems that can be modeled as bi-level problems on graphs. Moreover, we foreseen interesting future research directions, thus we also put forward a brief research agenda for this topic. The key observation from our research that can shape future research is that the fitness function evaluation time using the inferences made by the GNN model was in the order of milliseconds, which points to an opportunity and a need for novel heuristics that 1) can cope well with noisy fitness function values provided by deep learning models, and 2) can use the significantly enlarged efficiency of the evaluation step to explore the search space effectively (rather than efficiently). This opens a new avenue for a modern class of metaheuristics that are crafted for use with AI-powered predictors.

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a $\ell_2$-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of $\sim 13 \% / 5\%$ relative to previous approaches.

Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments and explore the practical consideration of model hyper-parameter selection and reward tuning. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.

The application of deep learning to non-stationary temporal datasets can lead to overfitted models that underperform under regime changes. In this work, we propose a modular machine learning pipeline for ranking predictions on temporal panel datasets which is robust under regime changes. The modularity of the pipeline allows the use of different models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks, with and without feature engineering. We evaluate our framework on financial data for stock portfolio prediction, and find that GBDT models with dropout display high performance, robustness and generalisability with reduced complexity and computational cost. We then demonstrate how online learning techniques, which require no retraining of models, can be used post-prediction to enhance the results. First, we show that dynamic feature projection improves robustness by reducing drawdown in regime changes. Second, we demonstrate that dynamical model ensembling based on selection of models with good recent performance leads to improved Sharpe and Calmar ratios of out-of-sample predictions. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility.

Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations. Here, we simplify such difficulties for a class of sparse or structured symmetric positive-definite matrices with the affine-invariant metric. We do so by proposing a generalized version of the Riemannian normal coordinates that dynamically orthonormalizes the metric and locally converts the problem into an unconstrained problem in the Euclidean space. We use our approach to simplify existing approaches for structured covariances and develop matrix-inverse-free $2^\text{nd}$-order optimizers for deep learning with low precision by using only matrix multiplications. Code: https://github.com/yorkerlin/StructuredNGD-DL

Inter-city highway transportation is significant for urban life. As one of the key functions in intelligent transportation system (ITS), traffic evaluation always plays significant role nowadays, and daily traffic flow prediction still faces challenges at network-wide toll stations. On the one hand, the data imbalance in practice among various locations deteriorates the performance of prediction. On the other hand, complex correlative spatio-temporal factors cannot be comprehensively employed in long-term duration. In this paper, a prediction method is proposed for daily traffic flow in highway domain through spatio-temporal deep learning. In our method, data normalization strategy is used to deal with data imbalance, due to long-tail distribution of traffic flow at network-wide toll stations. And then, based on graph convolutional network, we construct networks in distinct semantics to capture spatio-temporal features. Beside that, meteorology and calendar features are used by our model in the full connection stage to extra external characteristics of traffic flow. By extensive experiments and case studies in one Chinese provincial highway, our method shows clear improvement in predictive accuracy than baselines and practical benefits in business.

Database administrators (DBAs) play a crucial role in managing, maintaining and optimizing a database system to ensure data availability, performance, and reliability. However, it is hard and tedious for DBAs to manage a large number of database instances (e.g., millions of instances on the cloud databases). Recently large language models (LLMs) have shown great potential to understand valuable documents and accordingly generate reasonable answers. Thus, we propose D-Bot, a LLM-based database administrator that can continuously acquire database maintenance experience from textual sources, and provide reasonable, well-founded, in-time diagnosis and optimization advice for target databases. This paper presents a revolutionary LLM-centric framework for database maintenance, including (i) database maintenance knowledge detection from documents and tools, (ii) tree of thought reasoning for root cause analysis, and (iii) collaborative diagnosis among multiple LLMs. Our preliminary experimental results that D-Bot can efficiently and effectively diagnose the root causes and our code is available at github.com/TsinghuaDatabaseGroup/DB-GPT.

Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expensive computational requirements. This challenge has recently reached another scale with the emergence of large language models (LLMs). In order to reduce both their memory footprint and latency, a promising technique is quantization. It consists in converting floating point representations to low bit-width fixed point representations, usually by assuming a uniform mapping onto a regular grid. This process, referred to in the literature as uniform quantization, may however be ill-suited as most DNN weights and activations follow a bell-shaped distribution. This is even worse on LLMs whose weight distributions are known to exhibit large, high impact, outlier values. In this work, we propose an improvement over the most commonly adopted way to tackle this limitation in deep learning models quantization, namely, non-uniform quantization. NUPES leverages automorphisms to preserve the scalar multiplications. Such transformations are derived from power functions. However, the optimization of the exponent parameter and weight values remains a challenging and novel problem which could not be solved with previous post training optimization techniques which only learn to round up or down weight values in order to preserve the predictive function. We circumvent this limitation with a new paradigm: learning new quantized weights over the entire quantized space. Similarly, we enable the optimization of the power exponent, i.e. the optimization of the quantization operator itself during training by alleviating all the numerical instabilities. The resulting predictive function is compatible with integer-only low-bit inference. We show the ability of the method to achieve state-of-the-art compression rates in both, data-free and data-driven configurations.

Inspired by the recent success of large language models (LLMs) like ChatGPT, researchers start to explore the adoption of LLMs for agile hardware design, such as generating design RTL based on natural-language instructions. However, in existing works, their target designs are all relatively simple and in a small scale, and proposed by the authors themselves, making a fair comparison among different LLM solutions challenging. In addition, many prior works only focus on the design correctness, without evaluating the design qualities of generated design RTL. In this work, we propose an open-source benchmark named RTLLM, for generating design RTL with natural language instructions. To systematically evaluate the auto-generated design RTL, we summarized three progressive goals, named syntax goal, functionality goal, and design quality goal. This benchmark can automatically provide a quantitative evaluation of any given LLM-based solution. Furthermore, we propose an easy-to-use yet surprisingly effective prompt engineering technique named self-planning, which proves to significantly boost the performance of GPT-3.5 in our proposed benchmark.

With the big popularity and success of Judea Pearl's original causality book, this review covers the main topics updated in the second edition in 2009 and illustrates an easy-to-follow causal inference strategy in a forecast scenario. It further discusses some potential benefits and challenges for causal inference with time series forecasting when modeling the counterfactuals, estimating the uncertainty and incorporating prior knowledge to estimate causal effects in different forecasting scenarios.

Ocean modeling is a powerful tool for simulating the physical, chemical, and biological processes of the ocean, which is the foundation for marine science research and operational oceanography. Modern numerical ocean modeling mainly consists of governing equations and numerical algorithms. Nonlinear instability, computational expense, low reusability efficiency and high coupling costs have gradually become the main bottlenecks for the further development of numerical ocean modeling. Recently, artificial intelligence-based modeling in scientific computing has shown revolutionary potential for digital twins and scientific simulations, but the bottlenecks of numerical ocean modeling have not been further solved. Here, we present AI-GOMS, a large AI-driven global ocean modeling system, for accurate and efficient global ocean daily prediction. AI-GOMS consists of a backbone model with the Fourier-based Masked Autoencoder structure for basic ocean variable prediction and lightweight fine-tuning models incorporating regional downscaling, wave decoding, and biochemistry coupling modules. AI-GOMS has achieved the best performance in 30 days of prediction for the global ocean basic variables with 15 depth layers at 1/4{\deg} spatial resolution. Beyond the good performance in statistical metrics, AI-GOMS realizes the simulation of mesoscale eddies in the Kuroshio region at 1/12{\deg} spatial resolution and ocean stratification in the tropical Pacific Ocean. AI-GOMS provides a new backbone-downstream paradigm for Earth system modeling, which makes the system transferable, scalable and reusable.

Effective disaster response is critical for affected communities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and demands during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics:"health impact," "damage," and "evacuation." We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response and support their decision-making processes.

In this paper, we describe a new algorithm called Preferential Attachment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. Recently, several algorithms have obtained high accuracy on this problem. However, they have done so by relying on information about fake accounts' friendships or the content they share with others--the very things we seek to prevent. PreAttacK represents a significant departure from these approaches. We provide some of the first detailed distributional analyses of how new fake (and real) accounts first attempt to request friends after joining a major network (Facebook). We show that even before a new account has made friends or shared content, these initial friend request behaviors evoke a natural multi-class extension of the canonical Preferential Attachment model of social network growth. We use this model to derive a new algorithm, PreAttacK. We prove that in relevant problem instances, PreAttacK near-optimally approximates the posterior probability that a new account is fake under this multi-class Preferential Attachment model of new accounts' (not-yet-answered) friend requests. These are the first provable guarantees for fake account detection that apply to new users, and that do not require strong homophily assumptions. This principled approach also makes PreAttacK the only algorithm with provable guarantees that obtains state-of-the-art performance on new users on the global Facebook network, where it converges to AUC=0.9 after new users send + receive a total of just 20 not-yet-answered friend requests. For comparison, state-of-the-art benchmarks do not obtain this AUC even after observing additional data on new users' first 100 friend requests. Thus, unlike mainstream algorithms, PreAttacK converges before the median new fake account has made a single friendship (accepted friend request) with a human.

In this paper, we introduce a strategy for identifying textual saliency in large-scale language models applied to classification tasks. In visual networks where saliency is more well-studied, saliency is naturally localized through the convolutional layers of the network; however, the same is not true in modern transformer-stack networks used to process natural language. We adapt gradient-based saliency methods for these networks, propose a method for evaluating the degree of semantic coherence of each layer, and demonstrate consistent improvement over numerous other methods for textual saliency on multiple benchmark classification datasets. Our approach requires no additional training or access to labelled data, and is comparatively very computationally efficient.

Tracking and following objects of interest is critical to several robotics use cases, ranging from industrial automation to logistics and warehousing, to healthcare and security. In this paper, we present a robotic system to detect, track, and follow any object in real-time. Our approach, dubbed ``follow anything'' (FAn), is an open-vocabulary and multimodal model -- it is not restricted to concepts seen at training time and can be applied to novel classes at inference time using text, images, or click queries. Leveraging rich visual descriptors from large-scale pre-trained models (foundation models), FAn can detect and segment objects by matching multimodal queries (text, images, clicks) against an input image sequence. These detected and segmented objects are tracked across image frames, all while accounting for occlusion and object re-emergence. We demonstrate FAn on a real-world robotic system (a micro aerial vehicle) and report its ability to seamlessly follow the objects of interest in a real-time control loop. FAn can be deployed on a laptop with a lightweight (6-8 GB) graphics card, achieving a throughput of 6-20 frames per second. To enable rapid adoption, deployment, and extensibility, we open-source all our code on our project webpage at https://github.com/alaamaalouf/FollowAnything . We also encourage the reader the watch our 5-minutes explainer video in this https://www.youtube.com/watch?v=6Mgt3EPytrw .

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel methods. Our scheme is an approximate realization of the power method, where supervised learning is used to learn the next step of the power iteration. We show that the ground state properties of arbitrary gapped quantum hamiltonians can be reached with polynomial resources under the assumption that the supervised learning is efficient. Using kernel ridge regression, we provide numerical evidence that the learning assumption is verified by applying our scheme to find the ground states of several prototypical interacting many-body quantum systems, both in one and two dimensions, showing the flexibility of our approach.

This paper is devoted to studying the optimal expressive power of ReLU deep neural networks (DNNs) and its application in approximation via the Kolmogorov Superposition Theorem. We first constructively prove that any continuous piecewise linear functions on $[0,1]$, comprising $O(N^2L)$ segments, can be represented by ReLU DNNs with $L$ hidden layers and $N$ neurons per layer. Subsequently, we demonstrate that this construction is optimal regarding the parameter count of the DNNs, achieved through investigating the shattering capacity of ReLU DNNs. Moreover, by invoking the Kolmogorov Superposition Theorem, we achieve an enhanced approximation rate for ReLU DNNs of arbitrary width and depth when dealing with continuous functions in high-dimensional spaces.

Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data. Synthetic data generation is a promising solution to mitigate these risks, often relying on deep generative models such as Generative Adversarial Networks (GANs). However, recent studies have shown that diffusion models offer several advantages over GANs, such as generation of more realistic synthetic data and stable training in generating data modalities, including image, text, and sound. In this work, we investigate the potential of diffusion models for generating realistic mixed-type tabular EHRs, comparing TabDDPM model with existing methods on four datasets in terms of data quality, utility, privacy, and augmentation. Our experiments demonstrate that TabDDPM outperforms the state-of-the-art models across all evaluation metrics, except for privacy, which confirms the trade-off between privacy and utility.

Reinforcement learning (RL) under changing environment models many real-world applications via nonstationary Markov Decision Processes (MDPs), and hence gains considerable interest. However, theoretical studies on nonstationary MDPs in the literature have mainly focused on tabular and linear (mixture) MDPs, which do not capture the nature of unknown representation in deep RL. In this paper, we make the first effort to investigate nonstationary RL under episodic low-rank MDPs, where both transition kernels and rewards may vary over time, and the low-rank model contains unknown representation in addition to the linear state embedding function. We first propose a parameter-dependent policy optimization algorithm called PORTAL, and further improve PORTAL to its parameter-free version of Ada-PORTAL, which is able to tune its hyper-parameters adaptively without any prior knowledge of nonstationarity. For both algorithms, we provide upper bounds on the average dynamic suboptimality gap, which show that as long as the nonstationarity is not significantly large, PORTAL and Ada-PORTAL are sample-efficient and can achieve arbitrarily small average dynamic suboptimality gap with polynomial sample complexity.

Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling. To resolve this restriction, weakly supervised CRL methods were introduced. To evaluate CRL performance, four existing datasets, Pendulum, Flow, CelebA(BEARD) and CelebA(SMILE), are utilized. However, existing CRL datasets are limited to simple graphs with few generative factors. Thus we propose two new datasets with a larger number of diverse generative factors and more sophisticated causal graphs. In addition, current real datasets, CelebA(BEARD) and CelebA(SMILE), the originally proposed causal graphs are not aligned with the dataset distributions. Thus, we propose modifications to them.

Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences. State-of-the-art approaches to this problem rely on Ordinary Differential Equations (ODEs) which are known to be slow and often require additional features to handle missing values. To address this issue, we propose a novel model using Graphs for Forecasting Irregularly Sampled Time Series with missing values which we call GraFITi. GraFITi first converts the time series to a Sparsity Structure Graph which is a sparse bipartite graph, and then reformulates the forecasting problem as the edge weight prediction task in the graph. It uses the power of Graph Neural Networks to learn the graph and predict the target edge weights. GraFITi has been tested on 3 real-world and 1 synthetic irregularly sampled time series dataset with missing values and compared with various state-of-the-art models. The experimental results demonstrate that GraFITi improves the forecasting accuracy by up to 17% and reduces the run time up to 5 times compared to the state-of-the-art forecasting models.

Code summarization aims to generate concise natural language descriptions for source code. The prevailing approaches adopt transformer-based encoder-decoder architectures, where the Abstract Syntax Tree (AST) of the source code is utilized for encoding structural information. However, ASTs are much longer than the corresponding source code, and existing methods ignore this size constraint by directly feeding the entire linearized AST into the encoders. This simplistic approach makes it challenging to extract truly valuable dependency relations from the overlong input sequence and leads to significant computational overhead due to self-attention applied to all nodes in the AST. To address this issue effectively and efficiently, we present a model, AST-MHSA that uses multi-head attention to extract the important semantic information from the AST. The model consists of two main components: an encoder and a decoder. The encoder takes as input the abstract syntax tree (AST) of the code and generates a sequence of hidden states. The decoder then takes these hidden states as input and generates a natural language summary of the code. The multi-head attention mechanism allows the model to learn different representations of the input code, which can be combined to generate a more comprehensive summary. The model is trained on a dataset of code and summaries, and the parameters of the model are optimized to minimize the loss between the generated summaries and the ground-truth summaries.

Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest. The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which is a notoriously hard problem. In this work, we present a large-scale analysis of common temporal rollout strategies, identifying the neglect of non-dominant spatial frequency information, often associated with high frequencies in PDE solutions, as the primary pitfall limiting stable, accurate rollout performance. Based on these insights, we draw inspiration from recent advances in diffusion models to introduce PDE-Refiner; a novel model class that enables more accurate modeling of all frequency components via a multistep refinement process. We validate PDE-Refiner on challenging benchmarks of complex fluid dynamics, demonstrating stable and accurate rollouts that consistently outperform state-of-the-art models, including neural, numerical, and hybrid neural-numerical architectures. We further demonstrate that PDE-Refiner greatly enhances data efficiency, since the denoising objective implicitly induces a novel form of spectral data augmentation. Finally, PDE-Refiner's connection to diffusion models enables an accurate and efficient assessment of the model's predictive uncertainty, allowing us to estimate when the surrogate becomes inaccurate.

This paper introduces FALL-E, a foley synthesis system and its training/inference strategies. The FALL-E model employs a cascaded approach comprising low-resolution spectrogram generation, spectrogram super-resolution, and a vocoder. We trained every sound-related model from scratch using our extensive datasets, and utilized a pre-trained language model. We conditioned the model with dataset-specific texts, enabling it to learn sound quality and recording environment based on text input. Moreover, we leveraged external language models to improve text descriptions of our datasets and performed prompt engineering for quality, coherence, and diversity. FALL-E was evaluated by an objective measure as well as listening tests in the DCASE 2023 challenge Task 7. The submission achieved the second place on average, while achieving the best score for diversity, second place for audio quality, and third place for class fitness.  ( 2 min )

When applied to autonomous vehicle (AV) settings, action recognition can enhance an environment model's situational awareness. This is especially prevalent in scenarios where traditional geometric descriptions and heuristics in AVs are insufficient. However, action recognition has traditionally been studied for humans, and its limited adaptability to noisy, un-clipped, un-pampered, raw RGB data has limited its application in other fields. To push for the advancement and adoption of action recognition into AVs, this work proposes a novel two-stage action recognition system, termed RALACs. RALACs formulates the problem of action recognition for road scenes, and bridges the gap between it and the established field of human action recognition. This work shows how attention layers can be useful for encoding the relations across agents, and stresses how such a scheme can be class-agnostic. Furthermore, to address the dynamic nature of agents on the road, RALACs constructs a novel approach to adapting Region of Interest (ROI) Alignment to agent tracks for downstream action classification. Finally, our scheme also considers the problem of active agent detection, and utilizes a novel application of fusing optical flow maps to discern relevant agents in a road scene. We show that our proposed scheme can outperform the baseline on the ICCV2021 Road Challenge dataset and by deploying it on a real vehicle platform, we provide preliminary insight to the usefulness of action recognition in decision making.  ( 3 min )

Learning disentangled representations with variational autoencoders (VAEs) is often attributed to the regularisation component of the loss. In this work, we highlight the interaction between data and the reconstruction term of the loss as the main contributor to disentanglement in VAEs. We show that standard benchmark datasets have unintended correlations between their subjective ground-truth factors and perceived axes in the data according to typical VAE reconstruction losses. Our work exploits this relationship to provide a theory for what constitutes an adversarial dataset under a given reconstruction loss. We verify this by constructing an example dataset that prevents disentanglement in state-of-the-art frameworks while maintaining human-intuitive ground-truth factors. Finally, we re-enable disentanglement by designing an example reconstruction loss that is once again able to perceive the ground-truth factors. Our findings demonstrate the subjective nature of disentanglement and the importance of considering the interaction between the ground-truth factors, data and notably, the reconstruction loss, which is under-recognised in the literature.  ( 2 min )

In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient Quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development.  ( 2 min )

Convolutional Neural Networks (CNNs) are models that are utilized extensively for the hierarchical extraction of features. Vision transformers (ViTs), through the use of a self-attention mechanism, have recently achieved superior modeling of global contextual information compared to CNNs. However, to realize their image classification strength, ViTs require substantial training datasets. Where the available training data are limited, current advanced multi-layer perceptrons (MLPs) can provide viable alternatives to both deep CNNs and ViTs. In this paper, we developed the SGU-MLP, a learning algorithm that effectively uses both MLPs and spatial gating units (SGUs) for precise land use land cover (LULC) mapping. Results illustrated the superiority of the developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based models, including HybridSN, ResNet, iFormer, EfficientFormer and CoAtNet. The proposed SGU-MLP algorithm was tested through three experiments in Houston, USA, Berlin, Germany and Augsburg, Germany. The SGU-MLP classification model was found to consistently outperform the benchmark CNN and CNN-ViT-based algorithms. For example, for the Houston experiment, SGU-MLP significantly outperformed HybridSN, CoAtNet, Efficientformer, iFormer and ResNet by approximately 15%, 19%, 20%, 21%, and 25%, respectively, in terms of average accuracy. The code will be made publicly available at https://github.com/aj1365/SGUMLP  ( 2 min )

Epilepsy is a prevalent neurological disorder characterized by recurrent and unpredictable seizures, necessitating accurate prediction for effective management and patient care. Application of machine learning (ML) on electroencephalogram (EEG) recordings, along with its ability to provide valuable insights into brain activity during seizures, is able to make accurate and robust seizure prediction an indispensable component in relevant studies. In this research, we present a comprehensive comparative analysis of five machine learning models - Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB) - for the prediction of epileptic seizures using EEG data. The dataset underwent meticulous preprocessing, including cleaning, normalization, outlier handling, and oversampling, ensuring data quality and facilitating accurate model training. These preprocessing techniques played a crucial role in enhancing the models' performance. The results of our analysis demonstrate the performance of each model in terms of accuracy. The LR classifier achieved an accuracy of 56.95%, while GB and DT both attained 97.17% accuracy. RT achieved a higher accuracy of 98.99%, while the ET model exhibited the best performance with an accuracy of 99.29%. Our findings reveal that the ET model outperformed not only the other models in the comparative analysis but also surpassed the state-of-the-art results from previous research. The superior performance of the ET model makes it a compelling choice for accurate and robust epileptic seizure prediction using EEG data.  ( 3 min )

We replace the multiplication and sigmoid function of the conventional recurrent gate with addition and ReLU activation. This mechanism is designed to maintain long-term memory for sequence processing but at a reduced computational cost, thereby opening up for more efficient execution or larger models on restricted hardware. Recurrent Neural Networks (RNNs) with gating mechanisms such as LSTM and GRU have been widely successful in learning from sequential data due to their ability to capture long-term dependencies. Conventionally, the update based on current inputs and the previous state history is each multiplied with dynamic weights and combined to compute the next state. However, multiplication can be computationally expensive, especially for certain hardware architectures or alternative arithmetic systems such as homomorphic encryption. It is demonstrated that the novel gating mechanism can capture long-term dependencies for a standard synthetic sequence learning task while significantly reducing computational costs such that execution time is reduced by half on CPU and by one-third under encryption. Experimental results on handwritten text recognition tasks furthermore show that the proposed architecture can be trained to achieve comparable accuracy to conventional GRU and LSTM baselines. The gating mechanism introduced in this paper may enable privacy-preserving AI applications operating under homomorphic encryption by avoiding the multiplication of encrypted variables. It can also support quantization in (unencrypted) plaintext applications, with the potential for substantial performance gains since the addition-based formulation can avoid the expansion to double precision often required for multiplication.  ( 2 min )

The primary method for identifying mental disorders automatically has traditionally involved using binary classifiers. These classifiers are trained using behavioral data obtained from an interview setup. In this training process, data from individuals with the specific disorder under consideration are categorized as the positive class, while data from all other participants constitute the negative class. In practice, it is widely recognized that certain mental disorders share similar symptoms, causing the collected behavioral data to encompass a variety of attributes associated with multiple disorders. Consequently, attributes linked to the targeted mental disorder might also be present within the negative class. This data impurity may lead to sub-optimal training of the classifier for a mental disorder of interest. In this study, we investigate this hypothesis in the context of major depressive disorder (MDD) and post-traumatic stress disorder detection (PTSD). The results show that upon removal of such data impurity, MDD and PTSD detection performances are significantly improved.  ( 2 min )

A significant number of machine learning models are vulnerable to model extraction attacks, which focus on stealing the models by using specially curated queries against the target model. This task is well accomplished by using part of the training data or a surrogate dataset to train a new model that mimics a target model in a white-box environment. In pragmatic situations, however, the target models are trained on private datasets that are inaccessible to the adversary. The data-free model extraction technique replaces this problem when it comes to using queries artificially curated by a generator similar to that used in Generative Adversarial Nets. We propose for the first time, to the best of our knowledge, an adversary black box attack extending to a regression problem for predicting bounding box coordinates in object detection. As part of our study, we found that defining a loss function and using a novel generator setup is one of the key aspects in extracting the target model. We find that the proposed model extraction method achieves significant results by using reasonable queries. The discovery of this object detection vulnerability will support future prospects for securing such models.  ( 2 min )

Stroke is a significant cause of mortality and morbidity, necessitating early predictive strategies to minimize risks. Traditional methods for evaluating patients, such as Acute Physiology and Chronic Health Evaluation (APACHE II, IV) and Simplified Acute Physiology Score III (SAPS III), have limited accuracy and interpretability. This paper proposes a novel approach: an interpretable, attention-based transformer model for early stroke mortality prediction. This model seeks to address the limitations of previous predictive models, providing both interpretability (providing clear, understandable explanations of the model) and fidelity (giving a truthful explanation of the model's dynamics from input to output). Furthermore, the study explores and compares fidelity and interpretability scores using Shapley values and attention-based scores to improve model explainability. The research objectives include designing an interpretable attention-based transformer model, evaluating its performance compared to existing models, and providing feature importance derived from the model.  ( 2 min )

We examine whether symmetry can be used to defend tree-based ensemble classifiers such as gradient-boosting decision trees (GBDTs) against adversarial perturbation attacks. The idea is based on a recent symmetry defense for convolutional neural network classifiers (CNNs) that utilizes CNNs' lack of invariance with respect to symmetries. CNNs lack invariance because they can classify a symmetric sample, such as a horizontally flipped image, differently from the original sample. CNNs' lack of invariance also means that CNNs can classify symmetric adversarial samples differently from the incorrect classification of adversarial samples. Using CNNs' lack of invariance, the recent CNN symmetry defense has shown that the classification of symmetric adversarial samples reverts to the correct sample classification. In order to apply the same symmetry defense to GBDTs, we examine GBDT invariance and are the first to show that GBDTs also lack invariance with respect to symmetries. We apply and evaluate the GBDT symmetry defense for nine datasets against six perturbation attacks with a threat model that ranges from zero-knowledge to perfect-knowledge adversaries. Using the feature inversion symmetry against zero-knowledge adversaries, we achieve up to 100% accuracy on adversarial samples even when default and robust classifiers have 0% accuracy. Using the feature inversion and horizontal flip symmetries against perfect-knowledge adversaries, we achieve up to over 95% accuracy on adversarial samples for the GBDT classifier of the F-MNIST dataset even when default and robust classifiers have 0% accuracy.  ( 2 min )

This paper presents an investigation into machine learning techniques for violence detection in videos and their adaptation to a federated learning context. The study includes experiments with spatio-temporal features extracted from benchmark video datasets, comparison of different methods, and proposal of a modified version of the "Flow-Gated" architecture called "Diff-Gated." Additionally, various machine learning techniques, including super-convergence and transfer learning, are explored, and a method for adapting centralized datasets to a federated learning context is developed. The research achieves better accuracy results compared to state-of-the-art models by training the best violence detection model in a federated learning context.  ( 2 min )

We address the challenge of sound propagation simulations in $3$D virtual rooms with moving sources, which have applications in virtual/augmented reality, game audio, and spatial computing. Solutions to the wave equation can describe wave phenomena such as diffraction and interference. However, simulating them using conventional numerical discretization methods with hundreds of source and receiver positions is intractable, making stimulating a sound field with moving sources impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with moving sources, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 Pa to 0.10 Pa. Notably, our method signifies a paradigm shift as no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains. We anticipate that our findings will drive further exploration of deep neural operator methods, advancing research in immersive user experiences within virtual environments.  ( 2 min )

This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i.e., FASTA/FASTQ5). FASTA/FASTQ files have several current limitations, such as their large file sizes, slow processing speeds for mapping and alignment, and contextual dependencies. These challenges significantly hinder investigations and tasks that involve finding similar sequences. The solution lies in transforming sequences into an alternative representation that facilitates easier clustering into similar groups compared to the raw sequences themselves. By assigning a unique vector embedding to each short sequence, it is possible to more efficiently cluster and improve upon compression performance for the string representations of cDNA libraries. Furthermore, through learning alternative coordinate vector embeddings based on the contexts of codon triplets, we can demonstrate clustering based on amino acid properties. Finally, using this sequence embedding method to encode barcodes and cDNA sequences, we can improve the time complexity of the similarity search by coupling vector embeddings with an algorithm that determines the proximity of vectors in Euclidean space; this allows us to perform sequence similarity searches in a quicker and more modular fashion.  ( 2 min )

The multifactorial etiology of autism spectrum disorder (ASD) suggests that its study would benefit greatly from multimodal approaches that combine data from widely varying platforms, e.g., neuroimaging, genetics, and clinical characterization. Prior neuroimaging-genetic analyses often apply naive feature concatenation approaches in data-driven work or use the findings from one modality to guide posthoc analysis of another, missing the opportunity to analyze the paired multimodal data in a truly unified approach. In this paper, we develop a more integrative model for combining genetic, demographic, and neuroimaging data. Inspired by the influence of genotype on phenotype, we propose using an attention-based approach where the genetic data guides attention to neuroimaging features of importance for model prediction. The genetic data is derived from copy number variation parameters, while the neuroimaging data is from functional magnetic resonance imaging. We evaluate the proposed approach on ASD classification and severity prediction tasks, using a sex-balanced dataset of 228 ASD and typically developing subjects in a 10-fold cross-validation framework. We demonstrate that our attention-based model combining genetic information, demographic data, and functional magnetic resonance imaging results in superior prediction performance compared to other multimodal approaches.  ( 2 min )

The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25-35 $\mu$Jy/beam. We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP$_{50}$ of 67.5\% and 76.8\% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM  ( 3 min )

The capacity to identify and analyze protein-protein interactions, along with their internal modular organization, plays a crucial role in comprehending the intricate mechanisms underlying biological processes at the molecular level. We can learn a lot about the structure and dynamics of these interactions by using network analysis. We can improve our understanding of the biological roots of disease pathogenesis by recognizing network communities. This knowledge, in turn, holds significant potential for driving advancements in drug discovery and facilitating personalized medicine approaches for disease treatment. In this study, we aimed to uncover the communities within the variant B.1.1.529 (Omicron virus) using two proposed novel algorithm (ABCDE and ALCDE) and four widely recognized algorithms: Girvan-Newman, Louvain, Leiden, and Label Propagation algorithm. Each of these algorithms has established prominence in the field and offers unique perspectives on identifying communities within complex networks. We also compare the networks by the global properties, statistic summary, subgraph count, graphlet and validate by the modulaity. By employing these approaches, we sought to gain deeper insights into the structural organization and interconnections present within the Omicron virus network.  ( 2 min )

Phosphorylation is central to numerous fundamental cellular processes, influencing the onset and progression of a variety of diseases. Identification of phosphorylation sites is thus an important step for understanding the molecular mechanisms of cells and virus infection, which potentially leads to new therapeutic targets. In this study, we present PTransIPs, a novel deep learning model for the identification of phosphorylation sites. PTransIPs treats amino acids in protein sequences as words in natural language, extracting unique encodings based on the types along with position of amino acids in the sequence. It also incorporates embeddings from large pre-trained protein models as additional data inputs. PTransIPS is further trained on a combination model of convolutional neural network with residual connections and Transformer model equipped with multi-head attention mechanisms. At last, the model outputs classification results through a fully connected layer. The results of independent testing reveal that PTransIPs outperforms existing state-of-the-art methodologies, achieving AUROCs of 0.9232 and 0.9660 for identifying phosphorylated S/T and Y sites respectively. In addition, ablation studies prove that pretrained model embeddings contribute to the performance of PTransIPs. Furthermore, PTransIPs has interpretable amino acid preference, visible training process and shows generalizability on other bioactivity classification tasks. To facilitate usage, our code and data are publicly accessible at \url{https://github.com/StatXzy7/PTransIPs}.  ( 2 min )

While many studies have assessed the fairness of AI algorithms in the medical field, the causes of differences in prediction performance are often unknown. This lack of knowledge about the causes of bias hampers the efficacy of bias mitigation, as evidenced by the fact that simple dataset balancing still often performs best in reducing performance gaps but is unable to resolve all performance differences. In this work, we investigate the causes of gender bias in machine learning-based chest X-ray diagnosis. In particular, we explore the hypothesis that breast tissue leads to underexposure of the lungs and causes lower model performance. Methodologically, we propose a new sampling method which addresses the highly skewed distribution of recordings per patient in two widely used public datasets, while at the same time reducing the impact of label errors. Our comprehensive analysis of gender differences across diseases, datasets, and gender representations in the training set shows that dataset imbalance is not the sole cause of performance differences. Moreover, relative group performance differs strongly between datasets, indicating important dataset-specific factors influencing male/female group performance. Finally, we investigate the effect of breast tissue more specifically, by cropping out the breasts from recordings, finding that this does not resolve the observed performance gaps. In conclusion, our results indicate that dataset-specific factors, not fundamental physiological differences, are the main drivers of male--female performance gaps in chest X-ray analyses on widely used NIH and CheXpert Dataset.  ( 3 min )

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.  ( 3 min )

Causal inference is a crucial goal of science, enabling researchers to arrive at meaningful conclusions regarding the predictions of hypothetical interventions using observational data. Path models, Structural Equation Models (SEMs), and, more generally, Directed Acyclic Graphs (DAGs), provide a means to unambiguously specify assumptions regarding the causal structure underlying a phenomenon. Unlike DAGs, which make very few assumptions about the functional and parametric form, SEM assumes linearity. This can result in functional misspecification which prevents researchers from undertaking reliable effect size estimation. In contrast, we propose Super Learner Equation Modeling, a path modeling technique integrating machine learning Super Learner ensembles. We empirically demonstrate its ability to provide consistent and unbiased estimates of causal effects, its competitive performance for linear models when compared with SEM, and highlight its superiority over SEM when dealing with non-linear relationships. We provide open-source code, and a tutorial notebook with example usage, accentuating the easy-to-use nature of the method.

In today's rapidly evolving urban landscapes, efficient and accurate mapping of road infrastructure is critical for optimizing transportation systems, enhancing road safety, and improving the overall mobility experience for drivers and commuters. Yet, a formidable bottleneck obstructs progress - the laborious and time-intensive manual identification of intersections. Simply considering the shear number of intersections that need to be identified, and the labor hours required per intersection, the need for an automated solution becomes undeniable. To address this challenge, we propose a novel approach that leverages connected vehicle data and cutting-edge deep learning techniques. By employing geohashing to segment vehicle trajectories and then generating image representations of road segments, we utilize the YOLOv5 (You Only Look Once version 5) algorithm for accurate classification of both straight road segments and intersections. Experimental results demonstrate an impressive overall classification accuracy of 95%, with straight roads achieving a remarkable 97% F1 score and intersections reaching a 90% F1 score. This approach not only saves time and resources but also enables more frequent updates and a comprehensive understanding of the road network. Our research showcases the potential impact on traffic management, urban planning, and autonomous vehicle navigation systems. The fusion of connected vehicle data and deep learning models holds promise for a transformative shift in road infrastructure mapping, propelling us towards a smarter, safer, and more connected transportation ecosystem.

This paper surveys some recent developments in measures of association related to a new coefficient of correlation introduced by the author. A straightforward extension of this coefficient to standard Borel spaces (which includes all Polish spaces), overlooked in the literature so far, is proposed at the end of the survey.

Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations. Here, we simplify such difficulties for a class of sparse or structured symmetric positive-definite matrices with the affine-invariant metric. We do so by proposing a generalized version of the Riemannian normal coordinates that dynamically orthonormalizes the metric and locally converts the problem into an unconstrained problem in the Euclidean space. We use our approach to simplify existing approaches for structured covariances and develop matrix-inverse-free $2^\text{nd}$-order optimizers for deep learning with low precision by using only matrix multiplications. Code: https://github.com/yorkerlin/StructuredNGD-DL

In this short note, I show how to adapt to H\"{o}lder smoothness using normalized gradients in a black-box way. Moreover, the bound will depend on a novel notion of local H\"{o}lder smoothness. The main idea directly comes from Levy [2017].

Channel modelling is essential to designing modern wireless communication systems. The increasing complexity of channel modelling and the cost of collecting high-quality wireless channel data have become major challenges. In this paper, we propose a diffusion model based channel sampling approach for rapidly synthesizing channel realizations from limited data. We use a diffusion model with a U Net based architecture operating in the frequency space domain. To evaluate how well the proposed model reproduces the true distribution of channels in the training dataset, two evaluation metrics are used: $i)$ the approximate $2$-Wasserstein distance between real and generated distributions of the normalized power spectrum in the antenna and frequency domains and $ii)$ precision and recall metric for distributions. We show that, compared to existing GAN based approaches which suffer from mode collapse and unstable training, our diffusion based approach trains stably and generates diverse and high-fidelity samples from the true channel distribution. We also show that we can pretrain the model on a simulated urban macro-cellular channel dataset and fine-tune it on a smaller, out-of-distribution urban micro-cellular dataset, therefore showing that it is feasible to model real world channels using limited data with this approach.

Counter-adversarial system design problems have lately motivated the development of inverse Bayesian filters. For example, inverse Kalman filter (I-KF) has been recently formulated to estimate the adversary's Kalman-filter-tracked estimates and hence, predict the adversary's future steps. The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF). The companion paper proposed the theory of I-EKF (with and without unknown inputs) and I-KF (with unknown inputs). In this paper, we develop this theory for highly non-linear models, which employ second-order, Gaussian sum, and dithered forward EKFs. In particular, we derive theoretical stability guarantees for the inverse second-order EKF using the bounded non-linearity approach. To address the limitation of the standard I-EKFs that the system model and forward filter are perfectly known to the defender, we propose reproducing kernel Hilbert space-based EKF to learn the unknown system dynamics based on its observations, which can be employed as an inverse filter to infer the adversary's estimate. Numerical experiments demonstrate the state estimation performance of the proposed filters using recursive Cram\'{e}r-Rao lower bound as a benchmark.

Here, we show that the InfoNCE objective is equivalent to the ELBO in a new class of probabilistic generative model, the recognition parameterised model (RPM). When we learn the optimal prior, the RPM ELBO becomes equal to the mutual information (MI; up to a constant), establishing a connection to pre-existing self-supervised learning methods such as InfoNCE. However, practical InfoNCE methods do not use the MI as an objective; the MI is invariant to arbitrary invertible transformations, so using an MI objective can lead to highly entangled representations (Tschannen et al., 2019). Instead, the actual InfoNCE objective is a simplified lower bound on the MI which is loose even in the infinite sample limit. Thus, an objective that works (i.e. the actual InfoNCE objective) appears to be motivated as a loose bound on an objective that does not work (i.e. the true MI which gives arbitrarily entangled representations). We give an alternative motivation for the actual InfoNCE objective. In particular, we show that in the infinite sample limit, and for a particular choice of prior, the actual InfoNCE objective is equal to the ELBO (up to a constant); and the ELBO is equal to the marginal likelihood with a deterministic recognition model. Thus, we argue that our VAE perspective gives a better motivation for InfoNCE than MI, as the actual InfoNCE objective is only loosely bounded by the MI, but is equal to the ELBO/marginal likelihood (up to a constant).

Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential family distributions, as well as quasi-likelihood modeling for overdispersed count data, for example, and allows for categorical or grouped covariates as well as continuous covariates. A randomized group-regularized optimization problem is studied. The added randomization allows us to construct a post-selection likelihood which we show to be adequate for selective inference when conditioning on the event of the selection of the grouped covariates. This likelihood also provides a selective point estimator, accounting for the selection by the group lasso. Confidence regions for the regression parameters in the selected model take the form of Wald-type regions and are shown to have bounded volume. The selective inference method for grouped lasso is illustrated on data from the national health and nutrition examination survey while simulations showcase its behaviour and favorable comparison with other methods.

As data shift or new data become available, updating clinical machine learning models may be necessary to maintain or improve performance over time. However, updating a model can introduce compatibility issues when the behavior of the updated model does not align with user expectations, resulting in poor user-model team performance. Existing compatibility measures depend on model decision thresholds, limiting their applicability in settings where models are used to generate rankings based on estimated risk. To address this limitation, we propose a novel rank-based compatibility measure, $C^R$, and a new loss function that aims to optimize discriminative performance while encouraging good compatibility. Applied to a case study in mortality risk stratification leveraging data from MIMIC, our approach yields more compatible models while maintaining discriminative performance compared to existing model selection techniques, with an increase in $C^R$ of $0.019$ ($95\%$ confidence interval: $0.005$, $0.035$). This work provides new tools to analyze and update risk stratification models used in clinical care.

Random Search is one of the most widely-used method for Hyperparameter Optimization, and is critical to the success of deep learning models. Despite its astonishing performance, little non-heuristic theory has been developed to describe the underlying working mechanism. This paper gives a theoretical accounting of Random Search. We introduce the concept of \emph{scattering dimension} that describes the landscape of the underlying function, and quantifies the performance of random search. We show that, when the environment is noise-free, the output of random search converges to the optimal value in probability at rate $ \widetilde{\mathcal{O}} \left( \left( \frac{1}{T} \right)^{ \frac{1}{d_s} } \right) $, where $ d_s \ge 0 $ is the scattering dimension of the underlying function. When the observed function values are corrupted by bounded $iid$ noise, the output of random search converges to the optimal value in probability at rate $ \widetilde{\mathcal{O}} \left( \left( \frac{1}{T} \right)^{ \frac{1}{d_s + 1} } \right) $. In addition, based on the principles of random search, we introduce an algorithm, called BLiN-MOS, for Lipschitz bandits in doubling metric spaces that are also endowed with a probability measure, and show that BLiN-MOS achieves a regret rate of order $ \widetilde{\mathcal{O}} \left( T^{ \frac{d_z}{d_z + 1} } \right) $, where $d_z$ is the zooming dimension of the problem instance.

It is desirable for statistical models to detect signals of interest independently of their position. If the data is generated by some smooth process, this additional structure should be taken into account. We introduce a new class of neural networks that are shift invariant and preserve smoothness of the data: functional neural networks (FNNs). For this, we use methods from functional data analysis (FDA) to extend multi-layer perceptrons and convolutional neural networks to functional data. We propose different model architectures, show that the models outperform a benchmark model from FDA in terms of accuracy and successfully use FNNs to classify electroencephalography (EEG) data.

Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately. Despite the potential of multi-task learning to yield more powerful inference than single-task alternatives, prior work in this area has largely omitted uncertainty quantification. Our focus in this paper is a common multi-task problem in neuroimaging, where the goal is to understand the relationship between multiple cognitive task scores (or other subject-level assessments) and brain connectome data collected from imaging. We propose a framework for selective inference to address this problem, with the flexibility to: (i) jointly identify the relevant covariates for each task through a sparsity-inducing penalty, and (ii) conduct valid inference in a model based on the estimated sparsity structure. Our framework offers a new conditional procedure for inference, based on a refinement of the selection event that yields a tractable selection-adjusted likelihood. This gives an approximate system of estimating equations for maximum likelihood inference, solvable via a single convex optimization problem, and enables us to efficiently form confidence intervals with approximately the correct coverage. Applied to both simulated data and data from the Adolescent Brain Cognitive Development (ABCD) study, our selective inference methods yield tighter confidence intervals than commonly used alternatives, such as data splitting. We also demonstrate through simulations that multi-task learning with selective inference can more accurately recover true signals than single-task methods.

One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependencies of effects on their causes. Possible identifiability of SCMs given data depends on assumptions made on the noise variables and the functional classes in the SCM. For instance, in the LiNGAM model, the functional class is restricted to linear functions and the disturbances have to be non-Gaussian. In this work, we propose TSLiNGAM, a new method for identifying the DAG of a causal model based on observational data. TSLiNGAM builds on DirectLiNGAM, a popular algorithm which uses simple OLS regression for identifying causal directions between variables. TSLiNGAM leverages the non-Gaussianity assumption of the error terms in the LiNGAM model to obtain more efficient and robust estimation of the causal structure. TSLiNGAM is justified theoretically and is studied empirically in an extensive simulation study. It performs significantly better on heavy-tailed and skewed data and demonstrates a high small-sample efficiency. In addition, TSLiNGAM also shows better robustness properties as it is more resilient to contamination.

In the past few years, there has been considerable interest in two prominent approaches for Distributionally Robust Optimization (DRO): Divergence-based and Wasserstein-based methods. The divergence approach models misspecification in terms of likelihood ratios, while the latter models it through a measure of distance or cost in actual outcomes. Building upon these advances, this paper introduces a novel approach that unifies these methods into a single framework based on optimal transport (OT) with conditional moment constraints. Our proposed approach, for example, makes it possible for optimal adversarial distributions to simultaneously perturb likelihood and outcomes, while producing an optimal (in an optimal transport sense) coupling between the baseline model and the adversarial model.Additionally, the paper investigates several duality results and presents tractable reformulations that enhance the practical applicability of this unified framework.

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel methods. Our scheme is an approximate realization of the power method, where supervised learning is used to learn the next step of the power iteration. We show that the ground state properties of arbitrary gapped quantum hamiltonians can be reached with polynomial resources under the assumption that the supervised learning is efficient. Using kernel ridge regression, we provide numerical evidence that the learning assumption is verified by applying our scheme to find the ground states of several prototypical interacting many-body quantum systems, both in one and two dimensions, showing the flexibility of our approach.

Modern transportation planning relies heavily on accurate predictions of person and vehicle trips. However, traditional planning models often fail to account for the intricacies and dynamics of travel behavior, leading to less-than-optimal accuracy in these predictions. This study explores the potential of deep learning techniques to transform the way we approach trip predictions, and ultimately, transportation planning. Utilizing a comprehensive dataset from the National Household Travel Survey (NHTS), we developed and trained a deep learning model for predicting person and vehicle trips. The proposed model leverages the vast amount of information in the NHTS data, capturing complex, non-linear relationships that were previously overlooked by traditional models. As a result, our deep learning model achieved an impressive accuracy of 98% for person trip prediction and 96% for vehicle trip estimation. This represents a significant improvement over the performances of traditional transportation planning models, thereby demonstrating the power of deep learning in this domain. The implications of this study extend beyond just more accurate predictions. By enhancing the accuracy and reliability of trip prediction models, planners can formulate more effective, data-driven transportation policies, infrastructure, and services. As such, our research underscores the need for the transportation planning field to embrace advanced techniques like deep learning. The detailed methodology, along with a thorough discussion of the results and their implications, are presented in the subsequent sections of this paper.

We show that taking the width and depth to infinity in a deep neural network with skip connections, when branches are scaled by $1/\sqrt{depth}$ (the only nontrivial scaling), result in the same covariance structure no matter how that limit is taken. This explains why the standard infinite-width-then-depth approach provides practical insights even for networks with depth of the same order as width. We also demonstrate that the pre-activations, in this case, have Gaussian distributions which has direct applications in Bayesian deep learning. We conduct extensive simulations that show an excellent match with our theoretical findings.

Context: I'm teaching myself machine learning and right now I'm starting on the very core of it which is mathematics. For those who bought this course from Udemy, is this enough for real life ML problems? submitted by /u/Forsaken_Buy_7531 [link] [comments]  ( 9 min )

My observations and opinions on using Petals to run distributed LLMs, as a host and a user. https://yak.ventures/2023/08/11/distributed-llms-with-petals/ I'd be very interested to talk to anyone that is utilizing a distributed model for daily use or for an application and even more interested to talk to anyone running a model among friends or colleagues in the private mode. submitted by /u/Ruleryak [link] [comments]  ( 9 min )

Here submitted by /u/TommZ5 [link] [comments]  ( 8 min )

RL techniques in general have the ability to increase the time horizon of models since future rewards impact the Q value or advantage of the current action. My understanding of RLHF is a reward model is trained based on human feedback, and then the LLM is optimized to maximize the reward from the reward model. Is this correct? If so, does the reward model care about future rewards? Does this impact the time horizon? submitted by /u/30299578815310 [link] [comments]  ( 9 min )

I am new to this field and feeling quite confused. I need to use a DL-based Keras Embeddings technique with the Dense layer for text classification (specifically, a Binary Classification problem), along with TF-IDF featurization as input for the Random Forest Algorithm. However, my confusion arises from the fact that the Keras Embedding Layer also serves as a featurization technique. Therefore, I'm uncertain whether this layer should be used as input for the Random Forest or it has the capability to classify text on its own. Second question is that what can be the reason to use Dense Layer and What it is exactly here. submitted by /u/ZahidAlee [link] [comments]  ( 9 min )

Hello. Trying to make a plan so I can chip away at stuff day-by-day over the next few months/year(s). I was wondering if I've classified everything in this diagram in the correct way or if I'm missing anything ? Reason I ask here is I'm not too sure if I'm missing anything obsecure or if I've misinterpreted anything ? Thank you ! https://preview.redd.it/dqbgfbcthjhb1.png?width=4299&format=png&auto=webp&s=1ceb45c3e4237f2204151bfed7bf45b54e4a5d68 submitted by /u/EngineerOwn6160 [link] [comments]  ( 9 min )

I'm currently working through "Understanding Deep Learning" by Simon J.D. Prince. On page 403, he makes the following statement about regularization: Another possible explanation for the ease with which models are trained is that regularization makes the loss surface flatter and more convex. From my understanding, L2 regularization (or weight decay) indeed adds a convex term λ∣∣w∣∣2 to the loss function, smoothing it out. Additionally, the Hessian matrix becomes more positive with the addition of the regularization term 2λI, giving the function a more convex characteristic. However, I'm puzzled as to how other regularization methods like Dropout, L1/Lasso, or Early Stopping might lead to a similarly flatter and more convex loss surface. Can anyone offer insights or explanations on this? submitted by /u/spontanurlaub [link] [comments]  ( 9 min )

This years Neurips has been a rollercoaster for everyone involved. Petar Veličković says that in their AC batch 65% submitted no rebuttal or withdrew. https://twitter.com/PetarV_93/status/1689648854646575105 Xin Eric Wang says in their batch pre-rebuttal no papers had an avg score above an weak accept. https://twitter.com/xwang_lk/status/1686517898108674048 Will NeurIPS keep 25% acceptance rate? What do you think will happen to neurips in light of the above? Is this the end of big ML confs? ​ submitted by /u/SuchOccasion457 [link] [comments]  ( 9 min )

submitted by /u/individual_kex [link] [comments]  ( 8 min )

Hi! I have been trying to find a good guide of how best to implement a Sentence Transformers style model using Keras, but have not found anything :( I have managed to get something running, but I am not sure it is pretty and wanted to see if anyone know how to improve it or maybe has seen a nice implementation on the web? Here is my first draft https://gist.github.com/ydennisy/fec55fab84d107b72852ba2d2c2b61db submitted by /u/Suspicious_Dress_350 [link] [comments]  ( 9 min )

I can understand part of the statement if you are using Adam. Since the trainable params are much less, we are saving on optimizer states. However, even we are not actually updating the pretrained model, we still need to compute the graidients for backpropagation to the lower layer of the lora head. The memory usage of gradients would not decrease. Please correct me if I am wrong. submitted by /u/Chen806 [link] [comments]  ( 9 min )

Hi everyone! I'm working on a model using YOLO v8x to detect regions on identity cards, but it struggles with identifying address regions. This issue seems to stem from insufficient data. Would it be advisable to incorporate additional data containing addresses(other documents instead of identity card) to enhance the model's accuracy in detecting address regions? submitted by /u/Ordinary_Run_2513 [link] [comments]  ( 9 min )

I read that using multiple gpus can improve inference performance, but I'm not sure why for my inference, its actually slower as I increase tensor_parallel_size. I know data transfer overhead and limited parallelism could be potential issues, are there ways to rectify this vllm = LLM( model="mosaicml/mpt-7b-instruct", trust_remote_code=True, dtype="float16", tensor_parallel_size=1, gpu_memory_utilization=.95, ) CPU times: user 3.66 s, sys: 262 ms, total: 3.93 s Wall time: 1.11 s vllm = LLM( model="mosaicml/mpt-7b-instruct", trust_remote_code=True, dtype="float16", tensor_parallel_size=2, gpu_memory_utilization=.95, ) CPU times: user 65.5 ms, sys: 32.2 ms, total: 97.7 ms Wall time: 1.27 s ​ submitted by /u/candyman54 [link] [comments]  ( 9 min )

This is going to be a post about the challenges I faced while working with ChatGPT in my previous company and the things we did to overcome them over a 2+ month struggle. Check us out at www.twilix.io if anything below resonates with you and I hope you find some of it helpful. So to begin, in my previous company we invested a few months building a chatbot to help with user onboarding. At first everything was great, and we saw a 40% decrease in drop-off rates (which is significant given we were building a consumer facing app), but somehow over time this drop-off rate started creeping up again. Perplexed by the unexpected turn in metrics, management started to question the benefits of maintaining this chatbot and was skeptical that we were cherry picking examples to showcase its performance…  ( 10 min )

I'm currently in the process of developing a stable diffusion/latent diffusion model entirely from scratch. However, I'm a bit confused from the documentation of the original repositories (both from CompVis). My intention is to experiment with significantly smaller models and datasets while retaining the same architecture. Unfortunately, neither repository offers an official configuration for training the txt2img architecture.Through my exploration of the issues, I've observed that the training script provided by the latent diffusion repository does support txt2img (although an official configuration has not been made available yet). I'm curious if any of you might be familiar with better online resources or tutorials that can provide a clearer and more comprehensive understanding of the training process. submitted by /u/Arabum97 [link] [comments]  ( 9 min )

Paper: https://github.com/OpenGVLab/Multi-Modality-Arena Github: https://github.com/OpenGVLab/Multi-Modality-Arena Abstract: Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress in tackling complex multimodal tasks. Among these cutting-edge developments, Google's Bard stands out for its remarkable multimodal capabilities, promoting comprehensive comprehension and reasoning across various domains. This work presents an early and holistic evaluation of LVLMs' multimodal abilities, with a particular focus on Bard, by proposing a lightweight variant of LVLM-eHub, named Tiny LVLM-eHub. In comparison to the vanilla version, Tiny LVLM-eHub possesses several appealing properties. Firstly, it provides a systematic assessment of six categories of multimodal capabilities, including visual perception, visual knowledge acquisition, visual reasoning, visual commonsense, object hallucination, and embodied intelligence, through quantitative evaluation of 42 standard text-related visual benchmarks. Secondly, it conducts an in-depth analysis of LVLMs' predictions using the ChatGPT Ensemble Evaluation (CEE), which leads to a robust and accurate evaluation and exhibits improved alignment with human evaluation compared to the word matching approach. Thirdly, it comprises a mere 2.1K image-text pairs, facilitating ease of use for practitioners to evaluate their own offline LVLMs. Through extensive experimental analysis, this study demonstrates that Bard outperforms previous LVLMs in most multimodal capabilities except object hallucination, to which Bard is still susceptible. Tiny LVLM-eHub serves as a baseline evaluation for various LVLMs and encourages innovative strategies aimed at advancing multimodal techniques. https://preview.redd.it/i6x6p5bloihb1.jpg?width=1485&format=pjpg&auto=webp&s=7e91fe184844278b0a7e14090ae9aaef54b29f37 ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

I’m using GPT-2 for Sequence Classification. I want to understand the words or sequences that lead to the predictions. Can you point me towards any papers, repos or libraries? submitted by /u/how_the_turn_tablez [link] [comments]  ( 8 min )

I've been researching AI swarms, and it seems to make more sense to have a lot of smaller models doing tasks separately. Thoughts? submitted by /u/deepengineai [link] [comments]  ( 8 min )

Hi, I have written a post discussion whether or not the hidden size in transformers is an overkill. TLDR; I show that an embedding size of 2048 is too much to represent just one token like `is` but rather, it can encode an average 8 tokens with up-to 16 tokens almost losslessly. I think if we can design more compute efficient transformers with some of the ideas that I explore in the post. Of course this is not a proper research with ablation studies and empirical analysis. But I would love to hear your thoughts on this topic. submitted by /u/NaxAlpha [link] [comments]  ( 9 min )

Personal Shelf Inspector is an application that helps visually impaired people during their day-to-day shopping. The application is based on a simple neural network and was created as a part of the AI for Accesibility Hackathon in 2020 in Prague. We decided to build free tools that make shopping for visually impaired people more accessible. These tools can be implemented in any retail chain within the loyalty app or in-store. ​ https://preview.redd.it/ehoxec5l4hhb1.png?width=1072&format=png&auto=webp&s=c96a5dd326cbfc8dd9bde9d84d45167d172ea27d The Idea A visually impaired person only needs their smartphone to use our tools. Personal Shelf Inspector is a web application that reads the price and product name from a price tag. The algorithm selects the price tag closest to the centre of the photo and sends it to the model, which reads the price and product name on the price tag. Then it returns this information to the application, which appears as text on the screen. ​ https://preview.redd.it/e098rtcn4hhb1.png?width=845&format=png&auto=webp&s=73a216ebf987e476d52091717ac156ac56daa29b The voice-over built into the user's mobile phone reads this text aloud. The app also helps to read the banknote values and read from a live video. What could be other cool ideas and concepts to help making the world more accesible using AI and Machine Learning? Feel free to share comments and impressions in the comments submitted by /u/DataSentics [link] [comments]  ( 9 min )

In "InstructBLIP" paper, authors say: "We transform 26 datasets into the instruction tuning format" in order to create a general-purpose vision language model via instruction tuning. However, they did not provide details on how they did this transformation. At a first glance, three ways come to mind: They use ChatGPT/GPT-4 to automatically transform them. They define and code rules to automatically transform them. They manually transform them (highly improbable) Someone knows the answer? Thank you so much submitted by /u/jrodriguezortega [link] [comments]  ( 9 min )

We wrote a perspective on open source machine learning in computational chemistry in JCIM_JCTC. It was an incredible amount of work and I hope readers will find it useful and educational. https://pubs.acs.org/doi/10.1021/acs.jcim.3c00643 If you need a preprint, you can find it on Researchgate. https://www.researchgate.net/publication/372470285_Open-Source_Machine_Learning_in_Computational_Chemistry submitted by /u/poorgenes [link] [comments]  ( 9 min )

submitted by /u/hardmaru [link] [comments]  ( 8 min )

Does anybody have a good recommendation for an SBC with AI accelerator (NPU) where I could attach a camera and train some YOLO models on the device itself for object recognition? submitted by /u/LM1117 [link] [comments]  ( 8 min )

Link to survey submitted by /u/TommZ5 [link] [comments]  ( 8 min )

submitted by /u/micahdjt1221 [link] [comments]  ( 8 min )

This week in AI - provided by aibrews.com feel free to follow their newsletter News and Insights Anthropic released a new version of Claude Instant, which offers faster performance at a lower price, with improvements in quote extraction, multilingual support, and question answering. It hallucinates less and is more resistant to jailbreaks [Details]. Stability AI announced the release of StableCode, its first LLM generative AI product for coding [Details]. Researchers present AudioLDM 2, a framework that utilizes the same learning method for speech, music, and sound effect generation [Details | GitHub]. Researchers from CMU and others conducted tests on 14 large language models and found that OpenAI’s ChatGPT and GPT-4 were the most left-wing libertarian, while Meta’s LlaMA was the m…  ( 10 min )

submitted by /u/SplitYOLO [link] [comments]  ( 8 min )

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submitted by /u/Rightperson1 [link] [comments]  ( 8 min )

At my company, we collaborate closely with top-level executives from Fortune 500 companies and other industry leaders, helping them identify and secure the right partners for crucial digital transformation initiatives. When these executives present us with their project specifics, budgets, obstacles, and schedules, we take charge of finding the right partners for their RFP process, enhancing the entire workflow for efficiency and effectiveness. Currently, I have a collection of RFP projects and I’m keen on leveraging AI to simplify the task of identifying potential partners to call. I provided ChatGPT with all of my various project details and would inquire, ‘Which of my client projects align well with X company, and what are the reasons?’ OR “Would X company align with any of my projects?” The AI started off well, but eventually became confused and started making mistakes. Are there any systems available that could assist me in this project matching process? submitted by /u/Ajkrouse [link] [comments]  ( 9 min )

Hi, what VQA platforms do you all have experience with? What would you think would be the most promising platform at the moment, and in the future? I've been playing around with Google Vertex AI (https://console.cloud.google.com/vertex-ai/generative/) but the current results are ... meh! 🤷‍♂️ Any other recommendations? submitted by /u/emc [link] [comments]  ( 8 min )

A new AI algorithm has detected a potentially hazardous asteroid that had gone unnoticed by human observers, slated to fly by Earth. The algorithm, HelioLinc3D, was explicitly designed for the Vera Rubin Observatory currently under construction in Northern Chile.[1] The U.S. Defense Department has created a task force to evaluate and guide the application of generative artificial intelligence for national security purposes, amid an explosion of public interest in the technology.[2] China’s largest web and cloud providers (Alibaba, Baidu, ByteDance, and Tencent)are lining up to buy as many Nvidia GPUs as they can while they still can get their hands on them.[3] At Black Hat USA 2023, DARPA issued a call to top computer scientists, AI experts, software developers, and beyond to participate in the AI Cyber Challenge (AIxCC) – a two-year competition aimed at driving innovation at the nexus of AI and cybersecurity to create a new generation of cybersecurity tools.[4] Sources: [1] https://www.giantfreakinrobot.com/sci/ai-asteroids.html [2] https://www.c4isrnet.com/artificial-intelligence/2023/08/10/pentagon-establishes-task-force-lima-to-study-generative-ai-issues/ [3] https://www.theregister.com/2023/08/11/chinese_web_giants_nvidia/ [4] https://www.hstoday.us/industry/industry-news/darpa-ai-cyber-challenge-aims-to-secure-nations-most-critical-software/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

submitted by /u/crua9 [link] [comments]  ( 8 min )

I have developed this chrome extension named Lupin which allows you to ask your question about your current tab directly to chatGPT by analysing the page's body. For instance, if you're looking into an Amazon product, you can ask your question about it directly to Lupin. https://chrome.google.com/webstore/detail/lupin/kdfaiheakopcdabhlcnbmfjffanaedgm?hl=en&authuser=0 Right now, this is an open-beta phase, so I am open to any feedback. I have improved some aspects based on the feedback I received but I want to improve as much as possible before going for version 1.1 If you wanna join me on this crusade and work together, DM me. Amor Fati, AAC submitted by /u/AttilaTheHappyHun [link] [comments]  ( 9 min )

Hello, is there anywhere I can find .wav files to see examples about how would be the ideal type of samples I should provide my AI so it learns a more wide register of my voice? I didn't manage to find anything like that Sorry if it's a newbie question submitted by /u/Callumpi [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

I am planning to throw my algorithm into a pronto server which enable me to increase the number of parallel workers. Currently, I am going with 24 workers. I'd appreciate more suggestions. Here's the pastebin link with syntax highlighting. Here's my code - #Modified this code - https://github.com/DeepReinforcementLearning/DeepReinforcementLearningInAction/blob/master/Chapter%204/Ch4_book.ipynb #Also, modified this code - https://github.com/higgsfield/RL-Adventure-2/blob/master/1.actor-critic.ipynb # Also, modified this code - https://github.com/ericyangyu/PPO-for-Beginners/blob/9abd435771aa84764d8d0d1f737fa39118b74019/ppo.py#L151 # Got a lot of help from the subreddit - reinforcement_learning if __name__ == '__main__': import numpy as np import gymnasium as gym from gymnasium.wrappers im…  ( 11 min )

Hi everyone, we finally ended our journey through Dreamer, and we released the last version, Dreamer V3 in SheepRL. Our implementation follows closely the author's one, and is very well documented, with a blog post to explain the details and differences between this version and Dreamer V2. Together with Dreamer, we also have Plan2Explore with Dreamer v1 and v2. Finally, we completed the integration with Diambra, so you can try your agents on new (funnier) benchmarks. Check it out and feel free to contribute. Every feedback is appreciated :) submitted by /u/TrottoDng [link] [comments]  ( 9 min )

Two cool concepts - General Value Functions & Options. Seem to be for the same purpose. ​ What are the differences between these 2 strategies, and what are the benefits of each? Thanks! submitted by /u/Cultural-Average3959 [link] [comments]  ( 8 min )

Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation. When you translate from one language to another, you want your machine translation to be accurate, fluent, and most importantly contextual. Domain-specific and language-specific customizable terminology is a key requirement for many government and commercial organizations. Custom terminology […]  ( 5 min )

Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in the same way as human beings can. Recently, state-of-the-art architectures like the transformer architecture are used to achieve near-human performance on NLP downstream tasks like text summarization, text classification, entity recognition, […]  ( 11 min )

Source: ArabianBusiness Takeaways Artificial Intelligence (AI) continues to evolve at a rapid pace, with groundbreaking strides in generative capabilities playing a critical role in defining this ever-evolving landscape. One such transformative leap is the advent of Program-Aided Language models (PAL), an innovative solution that revolutionizes how Language Learning Models (LLMs) function. This article delves into… Read More »Pushing boundaries with Generative AI: How Program-aided Language model (PAL) enhances Large Language Models (LLMs) for superior AI performance The post Pushing boundaries with Generative AI: How Program-aided Language model (PAL) enhances Large Language Models (LLMs) for superior AI performance appeared first on Data Science Central.  ( 22 min )

submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

I'm working on a skin cancer detection app where I can upload a picture of a mole or other skin lesion and have it tell me if its cancerous and what type of cancer it is, and I downloaded the HAM10000 database for it which came with 5 CSV files. I kind of understand the metadata CSV file but the other 4 don't make sense to me. They have a bunch of numbers and either L or RGB at the end of the file names. Can someone help me make sense of these? submitted by /u/timing_snow [link] [comments]  ( 9 min )

submitted by /u/Philipp [link] [comments]  ( 8 min )

submitted by /u/intengineering [link] [comments]  ( 8 min )

The titles says it all - obviously it would be great to have a range of voices as in Elevenlabs for instance. If not, has anyone done this and found an easy way. submitted by /u/dextercool [link] [comments]  ( 8 min )

submitted by /u/PerryJ [link] [comments]  ( 8 min )

The singularity is nearer submitted by /u/Psytorpz [link] [comments]  ( 8 min )

Google today announced the launch of Project IDX, its foray into offering an AI-enabled browser-based development environment for building full-stack web and multiplatform apps.[1] NVIDIA today announced NVIDIA AI Workbench, a unified, easy-to-use toolkit that allows developers to quickly create, test and customize pretrained generative AI models on a PC or workstation.[2] IBM said on Wednesday it would host Meta Platforms’ artificial intelligence language program on its own enterprise AI platform, watsonx.[3] New high-tech microscope using AI successfully detects malaria in returning travelers.[4] Sources: [1] https://techcrunch.com/2023/08/08/google-launches-project-idx-a-new-ai-enabled-browser-based-development-environment/ [2] https://nvidianews.nvidia.com/news/nvidia-ai-workbench-speeds-adoption-of-custom-generative-ai-for-worlds-enterprises [3] https://www.reuters.com/technology/ibm-launch-metas-llama-2-watsonx-ai-platform-businesses-2023-08-09/ [4] https://medicalxpress.com/news/2023-08-high-tech-microscope-ai-successfully-malaria.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I first tried this experiment back in January and it kinda tripped me out. I used the wonder AI. When I tried the experiment with the Wombo dream AI the results were completely random. I wonder what the results would be with Midjourney. I later revisited the experiment in June with the wonder AI and again got intriguing results. Posting this just as an experiment in the hopes others will try it and see if it is repeatable and if other AI have more consistent results than others. It’s just an experiment, I don’t really care about your opinion I care about your results from trying this. submitted by /u/Sonic_Improv [link] [comments]  ( 9 min )

How do you think OpenAI implemented the function calling feature, It seems like another contextual generation piece from the look of it but any interesting ideas and papers around this topic? submitted by /u/neuro_boogie [link] [comments]  ( 8 min )

​ https://preview.redd.it/wl1gtcngnchb1.jpg?width=1500&format=pjpg&auto=webp&s=24e35d852603c6139fd67f79457ec593fbad99f7 If you're someone who's curious about or working with LLMs there's a cool panel discussion coming up: Comparing the pros and cons of using existing LLMs, prompt engineering, and fine-tuning on custom datasets for different enterprise use cases. Fine-Tuning LLMs: Exploring the advantages and challenges of fine-tuning LLMs on custom datasets to align with specific business objectives. Tools and platforms: Discussing the various tools and platforms to facilitate LLM implementation Overcoming Challenges: Addressing the challenges associated with adopting LLMs, including data privacy, creating high quality datasets, computational resources, ethical considerations, and the need for specialized expertise. Future Directions: Exploring emerging trends, advancements, and potential future applications of LLMs in the enterprise context. Here's the event info: https://www.eventbrite.com/e/large-language-models-for-enterprise-success-challenges-and-approaches-tickets-695089811337?aff=oddtdtcreator submitted by /u/UpstairsLeast7642 [link] [comments]  ( 9 min )

Github: https://github.com/e2b-dev/awesome-ai-agents and https://github.com/EmbraceAGI/Awesome-AGI submitted by /u/Singularian2501 [link] [comments]  ( 8 min )

Google and Universal Music are discussing licensing artists' voices and melodies to develop AI-generated songs fans can create and pay for, seeking to get ahead of the controversial "deepfake" music trend. Though some stars oppose their work being mimicked, artists could opt-in to receive royalties in a model akin to how YouTube now pays for user-generated content. For Google, AI music would boost its generative AI offerings against competitors. But significant ethical hurdles around consent and IP must still be addressed in developing a legitimate AI music market. submitted by /u/Yavero [link] [comments]  ( 9 min )

I am trying to work with the UCF crime dataset and want to use transformers for video activity recognition, Does anyone have pointers to example projects as to which ones are good starting points? submitted by /u/bluzkluz [link] [comments]  ( 8 min )

So Llama 2 sounds awesome, but I really wanted to run it locally on my Macbook Pro instead of on a Linux box with an NVIDIA GPU. So I put the llama.cpp GGML models into the XetHub Llama 2 repo so I can use the power of Llama 2 locally. It now takes me 5 seconds to mount Llama 2 and it loads the GGML model almost instantly. Here’s how I did it: Create an account: Go to xethub.com and Sign In with GitHub Quick start: Go to xethub.com/explore/quickstart and follow the Install & Setup steps (xethub.com/explore/install) pip install pyxet for Python SDK and CLI Set up authentication: Create a Personal Access Token and then run the login command from a Terminal so your ~/.xetconfig is set up with your login token. Here’s the code to get Llama 2 up and running on your Mac laptop in a few …  ( 12 min )

Hi Reddit folks, I wanted to share some benchmarking data I recently compiled running upstage_Llama-2-70b-instruct-v2 on two different hardware setups. If you'd like to see the spreadsheet with the raw data you can check out this link. Hardware Config #1: AWS g5.12xlarge - 4 x A10 w/ 96GB VRAM Hardware Config #2: Vultr - 1 x A100 w/ 80GB VRAM A few questions I wanted to answer: How does the inference speed (tokens/s) between these two configurations compare? How does the number of input tokens impact inference speed? How many input tokens can these machines handle before they start to hit OOM? How does 4-bit vs 8-bit quantization affect all of the above? Why this model? I chose upstage_Llama-2-70b-instruct-v2 because it's the current #1 performing OS model on HuggingFace's LLM…  ( 10 min )

Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot adaptable policies from scratch. In contrast to neural network adaption policies, where only model parameters are optimized, ARZ can build control algorithms with the full expressive power of a linear register machine. We evolve modular policies that tune their model parameters and alter their inference algorithm on-the-fly to adapt to sudden environmental changes. We demonstrate our method on a realistic simulated quadruped robot, for which we evolve safe control policies that avoid falling when individual limbs suddenly break. This is a challenging task in which two popular neural network baselines fail. Finally, we conduct a detailed analysis of our method on a novel and challenging non-stationary control task dubbed Cataclysmic Cartpole. Results confirm our findings that ARZ is significantly more robust to sudden environmental changes and can build simple, interpretable control policies. Paper: https://arxiv.org/abs/2307.16890 Video: https://youtu.be/sEFP1Hay4nE submitted by /u/VishDev [link] [comments]  ( 9 min )

submitted by /u/VishDev [link] [comments]  ( 8 min )

Machine perception uses advanced sensors to collect information about the surrounding scene for situational awareness. State-of-the-art machine perception using active sonar, radar, and LiDAR to enhance camera vision faces difficulties when the number of intelligent agents scales up. Exploiting omnipresent heat signals could be a new frontier for scalable perception. However, objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect’. Thermal vision thus has no specificity limited by information loss, whereas thermal ranging—crucial for navigation—has been elusive even when combined with artificial intelligence (AI). Here, we propose and experimentally demonstrate heat-assisted detection and ranging (HADAR) overcoming this open challenge of ghosting and benchmark it against AI-enhanced thermal sensing. HADAR not only sees texture and depth through the darkness as if it were day but also perceives decluttered physical attributes beyond RGB or thermal vision, paving the way to fully passive and physics-aware machine perception. We develop HADAR estimation theory and address its photonic shot-noise limits depicting information-theoretic bounds to HADAR-based AI performance. HADAR ranging at night beats thermal ranging and shows an accuracy comparable with RGB stereovision in daylight. Our automated HADAR thermography reaches the Cramér–Rao bound on temperature accuracy, beating existing thermography techniques. Our work leads to a disruptive technology that can accelerate the Fourth Industrial Revolution (Industry 4.0) with HADAR-based autonomous navigation and human–robot social interactions. Paper: https://www.nature.com/articles/s41586-023-06174-6 Video: https://youtu.be/WKrzmaixAC0 submitted by /u/VishDev [link] [comments]  ( 9 min )

I was watching this talk where they were showing that basically every task in machine learning has been replaced by the transformer architecture. For instance, where a convolution neural network might have been used for image recognition in the past, the predominant strategy now is just to use a transformer instead. How true is this? Is it worth learning any other architecture than transformers for current state of the art research? submitted by /u/Active-Confidence926 [link] [comments]  ( 9 min )

Hello, Is Latent ODE an imputation model? If so, how does it handle missing values in the case of irregular sampled time series data? Latent ODE - Latent ODEs for Irregularly-Sampled Time Series (https://arxiv.org/abs/1907.03907) submitted by /u/flaubart9 [link] [comments]  ( 8 min )

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. This major release adds sparse, hybrid and subindexes to the embeddings interface. It also makes significant improvements to the LLM pipeline workflow. See links below for more. GitHub: https://github.com/neuml/txtai Release Notes: https://github.com/neuml/txtai/releases/tag/v6.0.0 Article: https://medium.com/neuml/whats-new-in-txtai-6-0-7d93eeedf804 submitted by /u/davidmezzetti [link] [comments]  ( 9 min )

I’m looking to build functionality that would allow a user to specify topics to be notified about in the news, eg. “Tax law changes in New York”, and notify them of recently published news articles related to that topic. Would the ideal strategy be to find relating articles to topics, or topics relating to articles as they come in? What models would be ideal here? I’m fairly new to this, so any help would be appreciated. submitted by /u/ByteBuff [link] [comments]  ( 9 min )

Long time listener, first time caller. I am looking to spearhead the impending transition of understanding AI/ML at my organization and am looking for community suggestions in courses and bootcamps that could provide a deeper knowledge for some of my work projects in the future. Specifically, I feel unequipped in how to properly test and validate models. I have a computer science background with strong skills in data analytics and programming. I’ve also taken several introductory courses at a high level for AI. Does anyone have suggestions or experiences for 1-4 week long bootcamps or intensive courses? I prefer in-person (anywhere in US) but would also consider live-online remote courses. Price is not a concern. Thanks in advance. submitted by /u/DungeonsGalore [link] [comments]  ( 9 min )

so, there is an AI Hackathon coming up next week. This gonna be my first hackathon and wanna win too, however i donot know what to build in "health sector and Open tech". I have few experience in NLP. Kindly please suggest any which can be build in 24hrs period. submitted by /u/Suspicious-Row-8804 [link] [comments]  ( 9 min )

I'm basically trying to create an object detection system that learns the more objects it sees. My problem is that object detection requires actualy objects that the AI will be able to put into a certain category to identify the object. I want to make an AI that is able to find out that there is an object, but it shouldnt need to know what the object is. I want the AI to be able to find objects in an image in realtime without needing to know what the object is. It's supposed to grasp the concept of what an object is. Are there any methods or datasets that would make something like this possible? [link] [comments]  ( 9 min )

The complete code of this article and other articles about handling data issues is available in the accompanying notebook on GitHub. Data cleaning, a crucial step in machine learning, addresses challenges like mislabeled examples, outliers, and duplicates. Our newest article features: Cleanlab: A tool that uses confident learning techniques to detect data issues. Spotlight by Renumics: An advanced visualization tool to review and explore the data issues detected by Cleanlab. It provides interactive features like the Similarity Map for pinpointing problem clusters. The article showcases the integration of these tools using the CIFAR-100 dataset. It details: Detecting three main issues: label inconsistencies, outliers, and near-duplicates. Spotlight's interactive environment to review these detected problems. Using the similarity map to navigate and understand the data, making it easier to identify and address issues. Full Article: Navigate Data Issues: Interactively explore results of Cleanlab with Renumics Spotlight submitted by /u/DocBrownMS [link] [comments]  ( 9 min )

What are language retrieval models exactly? I've been hearing more and more of those in the context of not needing embeddings retrieval and putting useful text in the system prompt. How do they work in simple terms? submitted by /u/Specialist_Ice_5715 [link] [comments]  ( 8 min )

Seeing a flood of career related questions on here, most of which have been asked and answered ad nauseum before. Can we get a tag for them to filter out or compile them all in a weekly mega thread so it doesn’t clog up the main feed? submitted by /u/pavelysnotekapret [link] [comments]  ( 9 min )

Hey people, this time I am asking for your opinion. I am in automotive. I know ML very well due to personal projects but so far in my 5 years I never had the opportunity of applying it in the industry so I built expertise in classic CV Modelling instead. Now there is an opportunity to work on newer topics like data generation, dataset curation or even ML based Fusion. Nevertheless I also got the opportunity to do SLAM and it excites me a lot because of the many things involved in it and the possibility to use parallelization and so on. What could be a nice strategy here in your opinion and why? I thank you all! submitted by /u/tricostume [link] [comments]  ( 9 min )

would it be possible to train or fine-tune a small (1-3B) model who's sole purpose is to perform function calls? similar to how we have tiny models like replit-v2-3B that are super capable at specific things like code auto-complete . i know that's how openAI implemented function call was by fine-tuning gpt-3.5/4 but I'm thinking just a straight up base model trained to understand and excel at function calls (similar to Gorilla for apis) i'm thinking it would be a perfect "glue" for bigger LLM apps-- avoiding the need for external tools like langchain/quidance/etc... submitted by /u/LyPreto [link] [comments]  ( 9 min )

I'm new to RL, and I was trying to train an agent to move items in a 2D grid. The agent needs to output the row number, column number, and item index, and right now I'm modeling them as discrete actions. I am not sure what kind of agent to use to solve this problem. I tried PPO, but I'm not sure what the output of the policy module should be in this case. I'd be grateful for any help. submitted by /u/LostPigeon25 [link] [comments]  ( 9 min )

This project allows you to easily implement parallel training with the multiprocessing module. submitted by /u/NoteDancing [link] [comments]  ( 8 min )

Learn about the challenges of data privacy and security, and the potential of smart technologies in creating efficient, livable urban environments. The post Understanding the future of smart cities through data science appeared first on Data Science Central.  ( 20 min )

This content was given as a keynote at the Workshop of Applied Data Science for Healthcare and covered during a tutorial at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, a premier forum for advancement, education, and adoption of the discipline of knowledge discovering and data mining. Recent and noteworthy advancements in […] The post Microsoft at KDD 2023: Advancing health at the speed of AI appeared first on Microsoft Research.  ( 12 min )

In this post, we present a cross-account observability dashboard that provides a centralized view for monitoring SageMaker user activities and resources across multiple accounts. It allows the end-users and cloud management team to efficiently monitor what ML workloads are running, view the status of these workloads, and trace back different account activities at certain points of time.  ( 12 min )

A Traveling Salesman tour visits a list of destinations using the shortest path. There’s an obvious way to find the shortest path connecting N points: try all N! paths and see which one is shortest. Unfortunately, that might take a while. Texas has 254 counties, and so calculating a tour of Texas counties by brute […] Creating a Traveling Salesman Tour of Texas with Mathematica first appeared on John D. Cook.  ( 6 min )

A spherical cap is the portion of a sphere above some horizontal plane. For example, the polar ice cap of the earth is the region above some latitude. I mentioned in this post that the area above a latitude φ is where R is the earth’s radius. Latitude is the angle up from the equator. […] Area and volume of hypersphere cap first appeared on John D. Cook.  ( 5 min )

In high dimensions, randomly chosen vectors are very likely nearly orthogonal. I’ll unpack this a little bit then demonstrate it by simulation. Then I’ll look at what happens when we restrict our attention to points with positive coordinates. *** The lengths of vectors don’t contribute to the angles between them, so we may as well […] Random points in a high-dimensional orthant first appeared on John D. Cook.  ( 6 min )

The continuous neutering of models (the process of making the models less capable or reducing certain aspects of their functionality to prevent them from generating inappropriate, harmful, or sensitive content), can now be regarded as a substantial contributor to the Singularity date's delay: www.daystosingularity.com/estimation-details/ submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 8 min )

submitted by /u/Chipdoc [link] [comments]  ( 8 min )

Anyone know of a good AI tool that I can self feed my own music and have it generate similar tracks based on my style? Having a hard time finding something like this. Really just want to play around, super curious, tia submitted by /u/yakisobas_ghost [link] [comments]  ( 8 min )

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From Anthropic https://arxiv.org/abs/2308.03296 Studying Large Language Model Generalization with Influence Functions When trying to gain better visibility into a machine learning model in order to understand and mitigate the associated risks, a potentially valuable source of evidence is: which training examples most contribute to a given behavior? Influence functions aim to answer a counterfactual: how would the model's parameters (and hence its outputs) change if a given sequence were added to the training set? While influence functions have produced insights for small models, they are difficult to scale to large language models (LLMs) due to the difficulty of computing an inverse-Hessian-vector product (IHVP). We use the Eigenvalue-corrected Kronecker-Factored Approximate Curvature (EK-FAC) approximation to scale influence functions up to LLMs with up to 52 billion parameters. In our experiments, EK-FAC achieves similar accuracy to traditional influence function estimators despite the IHVP computation being orders of magnitude faster. We investigate two algorithmic techniques to reduce the cost of computing gradients of candidate training sequences: TF-IDF filtering and query batching. We use influence functions to investigate the generalization patterns of LLMs, including the sparsity of the influence patterns, increasing abstraction with scale, math and programming abilities, cross-lingual generalization, and role-playing behavior. Despite many apparently sophisticated forms of generalization, we identify a surprising limitation: influences decay to near-zero when the order of key phrases is flipped. Overall, influence functions give us a powerful new tool for studying the generalization properties of LLMs. submitted by /u/DataPhreak [link] [comments]  ( 9 min )

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I hear AI & ML used interchangeable, and a lot of people dispute the use of the term "AI", as defining "intelligence" can be a sticky wicket. "Machine learning" seems like a much clearer term, describing systems that can optimize themselves given an objective function & maybe training data (generalization). But, I know ML is just a subset of AI, so is there any extant AI that isn't ML? If not, what would AI that's not ML look like? submitted by /u/ZealousidealTomato74 [link] [comments]  ( 9 min )

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These brainy superheroes of the AI realm are ready to conquer intellectual challenges with a snap of their digital fingers, leaving us mere mortals feeling like puny amoebas in comparison. More ere: https://daystosingularity.com/2023/06/21/brainy-superheroes/ submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 8 min )

I’ve been kind of getting ChatGPT and Google Bard to generate subreddits, and look for me Cannabis sites, and shopping. (to be specific haha, well forums) It’s done a good job with some of the links, but a lot of times it kind of makes them up. Is there not an Ai that can deep dive or skim the web more accurately? Does it decipher filters like “time and date”, “availability”, “price”. More problems I’ve run into is it not being able to go really far back such as early internet or none indexed sites. Also noticed with Google and Bing they will give you the same results over and over (I assume I should have used “no repeats”) Google also will show me sold out items or items that aren’t actually on sale. It’ll show the item as say “on sale: $12.00” inspected the link- “$137” actually?? Any filter tips, or other Ai?? submitted by /u/Maelasae [link] [comments]  ( 9 min )

I saw a reel or short about an site that can do that easily but I didn't really care about at that time so I didn't save it I regret my decision soooo much can someone help me I have already wasted so much of my time wandering here n there submitted by /u/Inevitable-Mousse489 [link] [comments]  ( 8 min )

If you're creating voice-enabled products, I hope this will help you choose which model to use! I read the papers and docs for Bark and Tortoise TTS - two text-to-speech models that seemed pretty similar on the surface but are actually pretty different. Here's what Bark can do: It can synthesize natural, human-like speech in multiple languages. Bark can also generate music, sound effects, and other audio. The model supports generating laughs, sighs, and other non-verbal sounds to make speech more natural and human-sounding. I find these really compelling and these imperfections make the speech sound much more real. Check out an example here (scroll down to "pizza.webm"). Bark allows control over tone, pitch, speaker identity and other attributes through text prompts. The model learns directly from text-audio pairs. Whereas for Tortoise TTS: It excels at cloning voices using just short audio samples of a target speaker. This makes it easy to produce text in many distinct voices (like celebrities). I think voice cloning is the best use case for this tool. The quality of the synthesized voices is pretty high. Tortoise supports fine-grained control of speech characteristics like tone, emotion, pacing, etc through priming text. Tortoise is only trained on English and it's not capable of producing sound effects. Here's how they compare to the other speech-related models I've taken a look at so far: Model Best Use Cases Key Strengths Bark Voice assistants, audio generation Flexibility, multilingual Tortoise TTS Audiobooks, voice cloning Natural prosody, voice cloning AudioLDM (full guide) Voice assistants High-quality speech and SFX Whisper Transcription Accuracy, flexibility Free VC Voice conversion Retains speech style I have a full write-up here if you want to read more, it's about a 10-minute read. I also looked at the model inputs and outputs and speculated on some products you can build with each tool. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

submitted by /u/Hiversitize [link] [comments]  ( 8 min )

Here are fresh AI Updates for you ​ Nvidia has launched new Grace Hopper Superchip to boost Generative AI Norton introduces new AI Scam Detection Tool 'Genie' Google Working on 'Brain2Music' to create music from your brain Google and Universal Music deal over 'AI Deepfakes' Is Zoom using your data to train its AI ? ​ Stay tuned for more ​ submitted by /u/Agitated-Spell3979 [link] [comments]  ( 8 min )

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I am currently an AI/ML student but I have recently been thinking more and more about cognitive science. I was wondering if you know of any good resources that approach AI from the perspective of cognitive science submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

Researchers at the Massachusetts Institute of Technology (MIT) and the Dana-Farber Cancer Institute have discovered that the use of artificial intelligence (AI) could make it easier to determine the sites of origin for enigmatic cancers and enable doctors to choose more targeted treatments.[1] Meta disbands protein-folding team in shift towards commercial AI.[2] OpenAI has introduced GPTBot, a web crawler to improve AI models. GPTBot scrupulously filters out data sources that violate privacy and other policies.[3] Disney has created a task force to study artificial intelligence and how it can be applied across the entertainment conglomerate, even as Hollywood writers and actors battle to limit the industry’s exploitation of the technology.[4] Sources: [1] https://www.nature.com/articles/s41591-023-02482-6 [2] https://www.ft.com/content/919c05d2-b894-4812-aa1a-dd2ab6de794a [3] https://www.searchenginejournal.com/openai-launches-gptbot-how-to-restrict-access/493394/#close [4] https://www.reuters.com/technology/disney-creates-task-force-explore-ai-cut-costs-sources-2023-08-08/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I see lots of people say AI will not be able to do movies / TV because it hallucinates yada yada yada. But, movies / TV shows follow a clear script. Script being a generic formula that is taught to script writers the same way as music theory cheat sheet helps aspiring musicians to write songs. Script. Script can be turned into machine readable format. You can add commands how to render a movie on the basis of it. For example in a script you could have #Jack, telling the thing reading the script that we are talking of actor #Jack meaning it should tap into assets about jack which would reside in folder Jack. Jack meanwhile could be rendered by sub ai to fit the part. That helps us nail down the character so it wont be changing appearance wise in our script. The AI part here comes from…  ( 11 min )

submitted by /u/egusa [link] [comments]  ( 8 min )

Thanks any answers or musings - what are some technical limitations (eg computing / storage power/speed) that (1) limits AI's progress and (2) might be solved (and how), and (3) if solved, would make possible developments we can conceive of but not do yet? I'm just wondering if AI researchers forsee a kind of 'leap forward' and what are some obstacles? submitted by /u/OpenWaterRescue [link] [comments]  ( 8 min )

Hello! I am working off the VowpalWabbit example for explore_adf, just changing the cost function and actions but I get no learning. What I mean is that I train a model but when I ran the prediction, I just get an array of equivalent probabilities (0.25, 0.25, 0.25, 0.25). I have tried changing everything (making only one action to payoff for example) and still get the same error. Anyone has ran into a similar situation? Help please! submitted by /u/juanccs [link] [comments]  ( 9 min )

I am currently addressing a challenge within the domain of Reinforcement Learning. The particular issue revolves around a dynamic action space, where the set of potential actions available changes based on the context or state. In light of this, I am seeking guidance on the feasibility of utilizing the Deep Q-Network (DQN) approach to specifically identify permissible actions for distinct states. Furthermore, if the DQN approach is not applicable in this scenario, I would appreciate recommendations for alternative algorithms that could effectively address this issue. Additionally, I am considering the option of designing a single action space and employing negative reward to discourage the agent from pursuing unauthorized actions within specific states. submitted by /u/uonliaquat [link] [comments]  ( 9 min )

I've been building a multi-agent model of chess, where each side of the board is represented by a Deep Q Agent. I had it play 100k training games, but the loss scores increased over time, not decreased. I've got the (relatively short) implementation and the last few output graphs from the training--is there a problem with my model architecture or does it just need more training games, perhaps against a better opponent than itself? Here's the notebook file. Thanks in advance submitted by /u/lcmaier [link] [comments]  ( 9 min )

I'm trying to use a neurosymbolic approach to solve the Frozenlake enviroment, using also stable baselines 3. I used the TransformReward on the enviroment, and seems that it's working (changing the reward values). So here it is how it works the program: It calculates a reward per step based on the distance of the next state to the goal state. Also I tried adding some more constraints, like punishing if it stays on the same square or if it falls into a hole. The thing is that I don't know if I'm doing something wrong, so if someone can help me would be much appreciated. Here is part of the code, I'll omit the neuro symbolic part because it's irrelevant. The rewards are: ​ Taking a step in a direction that makes you near the goal: less than one (it depends on how near of the objecti…  ( 10 min )

I'm working on a RL Environment for my Masther Thesis where i try to explore the use of RL for Architectural Design. The environment looks like a 3D Grid of cubic 3D tiles or modules, and the agent can place tiles by choosing : Location (As x,y,z coordinates) , rotation (0 to 4 as multiples of 90 degrees around a z axis in the center) , and Tile Type (Im experimenting with many sets of tiles with different sizes). Then, I use Grasshopper3D to analyze the radiation that the interior surfaces and other metrics, that i use for my reward calculation. For this, the state of the environment is defined as a 3D Array with 2 channels. One for the Tile Types and one for the rotations. This is processed by a 3D CNN features extractor. The thing is that, I just realized that the array that represents the tile types as integers is actually categorical data, and I don't know how well this could work in a CNN. What i mean by this is that a tile=3 is not any more "anything" that a tile=1, it is just different. Am I doing something stupid then? Should i change it? I apologize if im saying something really dumb. I just got into RL few months ago : - ) submitted by /u/Direct-Software7378 [link] [comments]  ( 9 min )

submitted by /u/gwern [link] [comments]  ( 8 min )

The NeurIPS rebuttal has a 6000 character limit, however my rebuttal is way way over that. I was told by my supervisor that you could just comment chain onto the rebuttal to get past this, however that is not working. The deadline is in around 5 hours so I'm really in a big bind here. Does anyone have any insight about how to resolve this situation? submitted by /u/Pyramid_Jumper [link] [comments]  ( 9 min )

Hey guys, I am working as an SDET amd previously work as a developer. I am currently enrolled in Machine Learning masters online with job with Learning each course I am not sure how to get into the job market for this role. What projects should I create, kaggle seems to be just copy paste from each other.Any suggestions? submitted by /u/Latter_Ad_5679 [link] [comments]  ( 9 min )

It is something that sounds too much lately but I'm not sure about if it worths for those areas. submitted by /u/Otherwise-Bike4761 [link] [comments]  ( 8 min )

I’m looking to build functionality that would allow a user to specify topics to be notified about in the news, eg. “Tax law changes in New York”, and notify them of recently published news articles related to that topic. Would the ideal strategy be to find relating articles to topics, or topics relating to articles as they come in? What models would be ideal here? I’m fairly new to this, so any help would be appreciated. submitted by /u/ByteBuff [link] [comments]  ( 9 min )

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Moderators are doing a great job, but often by the time a post is deleted it already hit too many eyeballs. Now that everyone and their mom are into AI, does it make sense to switch to premoderation for new members and members who do not follow the rules of the subreddit? submitted by /u/lostmsu [link] [comments]  ( 9 min )

This project is the culmination of genuine effort and innovation by a team of dedicated professionals. We welcome constructive feedback and value your insights to help us improve and grow. Thank you for engaging with us respectfully. Hello AI and blockchain enthusiasts! I'm part of the team at Deep Engine AI, where we are working on an exciting project that involves building a Universal Adaptive Intelligence System (UAIS). One of our key concepts is what we're calling "Pervasive Swarm Learning." It's a blend of holistic swarm intelligence models, community-managed ecosystems, and innovative algorithms. We're reaching out to this knowledgeable community to get your thoughts, insights, or any innovative ideas that could help us refine and build out this concept. Whether you're an AI researcher, data scientist, blockchain expert, or simply someone interested in the field, your input could be invaluable to us. Here's a quick overview of what we're focusing on: Holistic Swarm Intelligence Models: Incorporating stochastic optimization, neuromorphic computing, and quantum-inspired algorithms for adaptability and resilience. Global Community-Managed Ecosystem: Enhancing our DAO with transparent and real-time community feedback loops. We believe in the power of collaboration and the collective intelligence of this community. If you have any insights, questions, or want to know more about what we're working on, please comment below or feel free to send me a private message If you want to dive deeper into our project, here's a link to our website. Thank you for taking the time to read this post. We look forward to hearing your thoughts and potentially collaborating with some of you! submitted by /u/deepengineai [link] [comments]  ( 9 min )

Two ways to answer this question: What is the average amount of hours? What is the amount of hours in a specific position that you are familiar with? I'm also wondering about non-academic ML PhDs who now work in industry. submitted by /u/Practical_Tea_3779 [link] [comments]  ( 8 min )

There have been many LLM inference solutions since the bloom of open-source LLMs. Most of the performant inference solutions are based on CUDA and optimized for NVIDIA GPUs. In the meantime, with the high demand for compute availability, it is useful to bring support to a broader class of hardware accelerators. AMD is one potential candidate. We build a project that makes it possible to compile LLMs and deploy them on AMD GPUs using ROCm and get competitive performance. More specifically, AMD Radeon™ RX 7900 XTX gives 80% of the speed of NVIDIA® GeForce RTX™ 4090 and 94% of the speed of NVIDIA® GeForce RTX™ 3090Ti for single batch Llama2-7B/13B 4bit inference. Besides ROCm, our Vulkan support allows us to generalize LLM deployment to other AMD devices, for example, a SteamDeck with an AMD APU. - Github: https://github.com/mlc-ai/mlc-llm/ - Blogpost describing the techniques: https://blog.mlc.ai/2023/08/09/Making-AMD-GPUs-competitive-for-LLM-inference ​ ​ submitted by /u/crowwork [link] [comments]  ( 9 min )

I am currently thinking about doing a PhD after I am done with my Master's thesis because the topic of my thesis is so fascinating. However, I put the possibility of doing a PhD aside because I was always more "hands-on" rather than academic / research focused. Would you say a PhD in the intersection of embedded systems + machine learning (maybe training a model "on the edge" with the sensor data of the embedded device) is beneficial regarding finding a job afterwards? submitted by /u/LM1117 [link] [comments]  ( 9 min )

I've become interested in how LLMs express emotions through their responses. Here's an example: [Q] = I am having a bad day [R] = I'm sorry to hear that you're having a bad day. Is there anything specific you'd like to talk about or any way I can help you feel better? Whether it's just a listening ear... I am aware that LLMs are not conscious and have no real understanding of emotions. But it is clear from the example that they can produce emotionally appropriate responses. Is there some kind of systematic test that can be automated to verify this? I.e. given a text-based query and response determine if the response is empathetic. submitted by /u/boringdude123 [link] [comments]  ( 9 min )

[P]I have imagery and lidar from trucks (cyclomedia). I would like to extract the latitude and longitude of fire hydrants from the images and point clouds.. I posted in r/computervision and they said it would be inaccurate to use imagery to locate fire hydrants. Do you have any tips on getting location from photographic images? Are there any open source neural nets for street view point clouds that can help? Is this an either/or problem or is it possible to use both data sources combined? submitted by /u/Zealousideal_Rub5826 [link] [comments]  ( 9 min )

Hi everyone, I have an interesting task in hand, and wanted some advice over here before getting my hands dirty. I have to compile a list of 15-20 papers, to systematically go from "what's a transformer" to bleeding edge research of multimodal and LLMs applications to robotics, dynamical systems, and RL (e.g. RT-2). The base assumption is that the reader will be a master student, who already has the basics of what deep learning and control theory are. Could you suggest possible cornerstone papers that could go in this list? If you could, this would guide my search a lot, and I would appreciate it. ​ ​ submitted by /u/Snekgineer [link] [comments]  ( 9 min )

https://mlsecops.com/resources/hacking-ai-h2o-exposes-entire-filesystem submitted by /u/FlyingTriangle [link] [comments]  ( 8 min )

What are some AI tools that you use often, that help you with your work/school or that you simply use for fun? submitted by /u/SadBlackTea [link] [comments]  ( 8 min )

Any recommendations for cool open-source ML projects that an intermediate Machine Learning engineer/researcher can contribute to? submitted by /u/Ahmed-Allam-220 [link] [comments]  ( 8 min )

I am currently an AI/ML student but I have recently been thinking more and more about cognitive science. I was wondering if you know of any good resources that approach AI from the perspective of cognitive science submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

Posted by Sandeep Tata, Software Engineer, Google Research, Athena Team The last few years have seen rapid progress in systems that can automatically process complex business documents and turn them into structured objects. A system that can automatically extract data from documents, e.g., receipts, insurance quotes, and financial statements, has the potential to dramatically improve the efficiency of business workflows by avoiding error-prone, manual work. Recent models, based on the Transformer architecture, have shown impressive gains in accuracy. Larger models, such as PaLM 2, are also being leveraged to further streamline these business workflows. However, the datasets used in academic literature fail to capture the challenges seen in real-world use cases. Consequently, academic b…  ( 93 min )

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Creative advertising has the potential to be revolutionized by generative AI (GenAI). You can now create a wide variation of novel images, such as product shots, by retraining a GenAI model and providing a few inputs into the model, such as textual prompts (sentences describing the scene and objects to be produced by the model). […]  ( 9 min )

Background In the previous part of this blog, we explored the limitations of GPT-4. In this post, we will explore if open source models can overcome the limitations of black box models. Specifically, we will consider the use of LLama2 in this scenario.  The llama 2 paper from Meta is very comprehensive.  Llama 2, is… Read More »Generative AI megatrends: implications of GPT-4 drift and open source models – part two The post Generative AI megatrends: implications of GPT-4 drift and open source models – part two appeared first on Data Science Central.  ( 19 min )

The previous post looked at cosine similarity for embeddings of words in vector spaces. Word embeddings like word2vec map words into high-dimensional vector spaces in such a way that related words correspond to vectors that are roughly parallel. Ideally the more similar the words, the smaller the angle between their corresponding vectors. The cosine similarity […] Cosine similarity does not satisfy the triangle inequality first appeared on John D. Cook.  ( 6 min )

Natural language processing represents words as high-dimensional vectors, on the order of 100 dimensions. For example, the glove-wiki-gigaword-50 set of word vectors contains 50-dimensional vectors, and the the glove-wiki-gigaword-200 set of word vectors contains 200-dimensional vectors. The intent is to represent words in such a way that the angle between vectors is related to similarity […] Angles between words first appeared on John D. Cook.  ( 7 min )

I’m kind of new to the world of machine/deep learning so cut me some slack here, but I was wondering the best ways to train models (in my case a transformer) without a GPU. I personally don’t even have a PC, I’ve been using a 2017 MacBook Air. I know deep learning models are quite computationally expensive and since I don’t have access to a GPU, how do I train models? I’ve read about cloud computing services like AWS, Google Colab, etc. but I was wondering what the best method was. Ideally free or as cheap as possible. submitted by /u/Present_Network1959 [link] [comments]  ( 9 min )

We are currently conducting a beta test for our Chrome Extension and we value external input. Our platform allows you to write and receive your gmail emails within the browser. You can also use AI to generate emails, without ever touching gmail or chatgpt. If you're interested in participating, please feel free to message or comment! submitted by /u/Live-Orange-8414 [link] [comments]  ( 9 min )

I have a regression model based on CNN, works reasonably well with less than 1M parameters. I am trying to check how Visual Transformer (ViT) will perform on this task, but due to lack of pooling in ViT, model size is considerably large (~10M parameters). Do ViT have anything equivalent to pooling to reduce number of parameters? If not then that reduces applicability of ViT to large models on large dataset dataset only. For smaller tasks with small dataset, CNN or Resnet are way more computation efficient. Or am I missing something? submitted by /u/Apprehensive-War8915 [link] [comments]  ( 9 min )

For our R&D team, we spent over a month trying to setup our pipeline. After that, we spend at least 5 days after R&D for to put a model into production without the required data pipelines that communicate with our model and the service. For training a model, the infrastructure maintain and manage it also needs to be built for around 2 weeks. Currently, our best solution is to offload the training process by purchasing a GPU and keeping it in the office. submitted by /u/potanees [link] [comments]  ( 9 min )

​ https://preview.redd.it/4t4jbi15rygb1.png?width=2291&format=png&auto=webp&s=f4eedf0d24dee2cbd040b3a19ab9610119b4001e Hi everyone! I want to share some interesting observations that indicate a very simple periodical weights resetting procedure could serve as an implicit regularization strategy for training DL models. This technique also shows potential connection with the Double Descent phenomenon. Here's the link to github etc: https://github.com/amcircle/weights-reset. As a co-author of this study, I must apologize in advance for its brevity. However, I sincerely hope it may prove useful to some. I would gladly respond to your queries and receive your criticism. Your personal experiences related to something similar would also be highly appreciated. submitted by /u/gregorivy [link] [comments]  ( 9 min )

Hi, I am going through research papers and noticed that most of the papers talk about the text conditioned image generation process (reverse diffusion process). The text and time encodings are added as additional channels to the UNet block. However, I am curious to know if any text encodings are used during the training process as well. Is there any preview of the training datasets that is available which is used in the training process ? or a code snippet that points out to the forward part of the training loop Thanks submitted by /u/kaskoraja [link] [comments]  ( 9 min )

Hi everyone, There has been a lot of work on benchmarking autoregressive LLMs, such as HF LLM Leaderboard, but I have not seen much work specifically on the relevancy of such LLMs for retrieval. There is a lot of talk about chat based on knowledge with solutions like llama_index, where LLMs both provide embeddings and answer based on most similar content, but embedding and answer generation need not be the same LLM. I saw the Massive Text Embedding Benchmark (MTEB) but it does not seem to contain a lot of information about the recent autoregressive LLMs. Are the recent autoregressive LLMs, e.g. Llama 2, actually performing better than Bidirectional LLMs such as BERT? Because if so, all the recent fancy chat with your documents projects could use much smaller models to do embedding extraction for retrieval and just call a fancy autoregressive LLM such as GPT4 for answer synthesis. submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

Hi I'm looking for an open source model that can take an image of an info graphic such as a pie chart, graph, etc, and provide a description of the information in that chart. For example the values of the x,y axis and their labels, weather the chart is increasing or decreasing. I've worked with image captioning models such as BLIP before as I have used them in projects involving stable diffusion, but this model doesn't give specifics about the information in the graph, just a brief overview. I know researches have worked on this problem in the past using the vistext dataset: https://news.mit.edu/2023/researchers-chart-captions-ai-vistext-0630 So far I'm thinking that it may come to me finetuning BLIP or equivalent to specialize on infographics instead. Thoughts? submitted by /u/UncleSammmm [link] [comments]  ( 9 min )

I have a significant amount of files that I would like to label for future reference. I've looked at software such as Photoprism or Librephoto which have object classification but they are based on a static model. I'd like something where I could label a few photos then generate similar matches where I can approve the good matches for reinforcement learning. I'm pretty sure I saw a demo like this at a code conference using Azure but I'm hoping for something self-hosted to avoid API fees. I was exploring coding something to do this for me but I don't want to put in the work if something with a UI exists already. This seemed like the best place to ask. submitted by /u/MZZXX [link] [comments]  ( 9 min )

Hi - I have trained a pytorch model that does some fairly simple object classification - The goal is distribute it as part of an app, that will pull information from a user's video. The videos are typically ~25-30 minutes and about 1GB in size, only a 600x600px square on the bottom right of the video is needed for the classification (it's a minimap in a video game) The app is electron based Ideally I want to input a video, and extract the labels from the cropped section once per second. ​ My current attempt involves converting the model to a tensorflowjs model, and rendering the video on a element, stretching it so only the minimap is visible on the canvas, running the model, saving the labels, and increasing the current time of the video by 1 second, and repeating until the video is done. ​ This seems like a terrible plan, but it's much better than the couple of other ideas I've tried (using ffmpeg to extract a frame every second for example) ​ Any advice appreciated! ​ Edit: just to clarify this will only ever be ran on Windows submitted by /u/FreddoRS [link] [comments]  ( 9 min )

Hello, I'm looking for a model (probably two models) that would: Summarize reviews (e.g. website review) to two/three words. Reuse these words or "review tokens" to tag reviews with similar content. Then if a review's content differs (e.g. cosine sim. of 0.2), another tag will be generated from the review that diverges. Is there anything like this on the "market"? submitted by /u/BartPetersyn [link] [comments]  ( 9 min )

Hello to everyone reading this. I am just about to finish Andrew NG's course 3 courses on ML specialization and I have had 2 courses on ML as well in my Business Intelligence Analytics studies at uni. Now I am extremely interested in ML but I see there are wide diaspora of different subfields you can focus on. I need to get into the job market as fast as possible. So can anyone guide me which aspect of ML should I give most of my time to practice and build portfolio that would translate well to interviews and hiring? Thank you submitted by /u/JaguarMoosa [link] [comments]  ( 9 min )

How would you proceed on a project in assessing the difficulty level of a question? I tried using lexicographic metrics like flesh-kincaid score, etc., but those did not yield proper results. Is there a good method I could use? Also, how could I assess the "readability" of a question, or in other words, how easy it is to understand what the question is asking. submitted by /u/uglyboi34 [link] [comments]  ( 9 min )

Hey y'all, this is a model I have been independently building for some time. It uses parts of OpenAI's Jukebox and HarmonAI's Dance Diffusion model. Overall it is a hierarchical latent diffusion modeland generates complete linked musical phrases at good quality. More information as well as examples can be found here: https://medium.com/@jeffsontagmusic/jukebox-diffusion-cbe22ff3cd47 Thanks! submitted by /u/jmoso13 [link] [comments]  ( 9 min )

A lot of my PhD work will be in using different types of ML/NN approaches to characterizing problems in my field. It's kind of weird, since for my undergrad I came from a more traditional science background where we research off papers that were written like 2-20 years ago. Since a lot of these architectures and whatever are updating so fast, I wanted to see if there's a good way to keep up with the latest information so my work wouldn't be outdated by the time I publish. Is there a general workflow that those of you in the field follow in regards to this? submitted by /u/This-Is-My-20th-Acc [link] [comments]  ( 9 min )

Hey everyone, I wrote a blog on LoRA and QLoRA. Hope it helps you in understanding the theory behind them 🤗 https://medium.com/@gitlostmurali/understanding-lora-and-qlora-the-powerhouses-of-efficient-finetuning-in-large-language-models-7ac1adf6c0cf If the above one is behind paywall, you can visit the blog here (https://gitlostmurali.com/machine-learning/data-science/lora-qlora) submitted by /u/Outlandish_MurMan [link] [comments]  ( 8 min )

Hey all, like the title says, I’m losing my voice due to an illness (Parkinson’s disease), and I would like to create an AI voice using recordings from 10 years ago. I used to be a prolific podcaster, and I have about 50 episodes of podcasts that I can use as input. Is this possible? What service or software can I use? My voice is beyond repair since Parkinson’s is a progressive disease. An AI voice would allow me to work and would open up new doors for me. Thank you! submitted by /u/NWMoney101 [link] [comments]  ( 9 min )

Candle is a minimalist ML framework for Rust Some of its features Examples of popular models: Whisper, Llama 2, Falcon, Bert, Starcoder WASM support, so you can run the models directly in the browser User-defined kernels, so you can use Flash Attention Similar syntax to PyTorch Data loaders Transformer utilities submitted by /u/hackerllama [link] [comments]  ( 9 min )

Hey all, I have a technical background, having studied engineering and ML at one of the world's leading universities. I really enjoyed it and did well, but long story short, since graduating (coming to 2 years) I have been working in a Family Office, doing things I don't feel are very related. I wanted to know what kind of things I can do to keep myself in the loop and continue developing my ML/DS skills in my spare time. Alternatively, ideas of projects I could have just to make sure I have a portfolio? submitted by /u/thegreatudini [link] [comments]  ( 9 min )

Typically, research papers and leaderboards only report the overall score on Measuring Massive Multitask Language Understanding (MMLU) and not per task performance. Hugging Face recently released detailed evaluation data that includes per task performance. I made a sortable leaderboard here https://huggingface.co/spaces/CoreyMorris/MMLU-by-task-Leaderboard . You can also make custom scatter plots on the site so you can explore the relationship between parameter count and performance. submitted by /u/corey1505 [link] [comments]  ( 9 min )

I've realized my technical understanding of explainability is a few years behind, having last focused on it with LIME and Shap. Does anyone have a survey reference they like for recent trends and updates in ML explainability? submitted by /u/balcell [link] [comments]  ( 8 min )

At first, with InstructGPT and ChatGPT, it looked like RLHF was the holy grail to successfully finetune LLMs on human preferences. Then, from May 2023 onwards, a trend of doing just SFT with high-quality data showed up (e.g. "LIMA: Less Is More for Alignment" https://arxiv.org/abs/2305.11206) as an alternative to doing RLHF. What's your opinion on these two narratives? Is RLHF likely to still be relevant even in the presence of SFT with high-quality data? submitted by /u/bornot2b [link] [comments]  ( 9 min )

Hi all, I am currently already working in the field of ML research at a big name medical research center. Our main focus is in application of ML methods with the focus on stroke diagnostics and treatment. Now, I am quite happy working here but my background is somewhat interdisciplinary. I have a bachelor's in Life science and a Master in bioinformatics. Because of this I always feel like I have to catch up to my colleagues when it comes to ML and in parts also computer science knowledge. It feels like there are a million things to learn and many small details to know that I am not even sure how to look up. I am curious what your experience has been if you were/are in a similar situation? How did you manage to catch up? submitted by /u/JuicyLambda [link] [comments]  ( 9 min )

Either I'm too new to the space, or I'm stating the obvious, but it seems that object detection performance is really low. The SOTA currently is 66% on COCO test-dev, which doesn't match how well it seems like AI is currently performing with self-driving cars, surveillance tech, and others. Am I missing something? submitted by /u/philipkd [link] [comments]  ( 9 min )

I. Introduction This paper aims to provide an in-depth explanation of representing and propagating wavefunctions in a hierarchical manner using Gaussian basis functions. Wavefunctions are mathematical descriptions of the quantum states of physical systems and are fundamental to quantum mechanics. However, representing complex wavefunctions for real-world quantum systems remains a key challenge. This paper proposes using multiple layers of Gaussian basis functions, with trainable amplitudes, to represent wavefunctions in a hierarchical fashion and enable wavefunction propagation between layers. Understanding wavefunction representation and propagation has significant implications in diverse fields like quantum computing, quantum chemistry, and materials science. Efficient wavefunction man…  ( 12 min )

I’ve been researching the Evol-Instruct datasets now for a few days and have decided I want to build my own out for a specific use case. I’ve read literally everything possible, admittedly not much outside of WizardLM and GeorgiaTech, but I’ve read it. I was hoping to discuss it here with smarter people. I’m seeing this as a way to use LLMs to generate great datasets. However, my use case doesn’t really exist in any models yet. Not thoroughly enough to produce a good Evol-Instruct set. So, I’m going to do that tomorrow. I’m going to use The Blokes WizardCoder-Guanaco 15b GPTQ version to train on my specific dataset - about 10GB of clean, really strong data I’ve spent 3-4 weeks putting together. In theory, I’ll use the Evol-Instruct script from WizardLM to generate the new dataset, and then I’ll apply that to whatever model I decide to use. There is a good chance I train my own on general Evol-Instruct datasets available now, and likely quite a large one. I’m looking for any tips, discussion, ideas, thoughts from the community. Cheers! submitted by /u/LoadingALIAS [link] [comments]  ( 9 min )

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All services I found made the blurry text even worse. Is there any which has good results for documents? submitted by /u/_SarahB_ [link] [comments]  ( 8 min )

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Hi there 👋, I'm excited to share a project I've been working on over the past few months! My primary goal is to create a service that will be beneficial for people. Please share your thoughts on this idea, and suggest any new features you think I should implement! Exciting Features: • Speak to Write: with this feature, you can speak your thoughts or information and the tool will transcribe it into text. The best part? You can then forward the transcribed text to any application with just one click. • Audio to Action Plan: the service can transform a received audio message into a structured list of elements or bullet points. This feature is especially useful for outlining an action plan or item list. • Speak in and Language: you can dictate an audio message in your native language, and the service will translate it into any other language, maintaining high translation quality—significantly better than Google Translate. • Meeting Transcripts & Summaries: the service is perfect for converting recorded audio from meetings into text and generating concise summaries. It supports the upload of users' files. Thank you for taking the time to check it out. I look forward to hearing your feedback. You can access the service by visiting this link: https://audionotes.ai submitted by /u/OneMoreSuperUser [link] [comments]  ( 9 min )

I have a killer project idea but it requires fully custom image generation. Character portraits. Any API like that out there? submitted by /u/thedarklord176 [link] [comments]  ( 8 min )

I just want a script to perform the task not AI itself so that I have something reliable. It always puzzles me why these things don't instantly pop up as services where I don't have to worry about even deploying the script (but that's another issue). submitted by /u/VLADIMIROVIC_L [link] [comments]  ( 8 min )

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Tutorial about creating images using ChatGPT. submitted by /u/SplitYOLO [link] [comments]  ( 8 min )

Hello, I'm currently editing videos using capcut, which is not ideal. I'm looking for an ai, that ideally : Finds me B-roll according to what I speak. Cuts "bad takes" out Good captions "TikTok style" Audio enhance. Do you guys know anything like this? Thank you! submitted by /u/Orlandostyler [link] [comments]  ( 8 min )

I've been using this today whilst I've been working and I found it pretty comical at first with the voice that talks to you, but now I'm starting to love it! I want it to talk more when it does talk. It feels like a nice break in the music to have the AI talk like a radio host. I'm sure some people would rather that not being a feature (if they use it at all), but I'd love for it to have some more comedic one-liners, possible news updates, and potentially traffic updates based on location and if it knows you're driving. Would be awesome! It's also a really good tool for if you want to listen to music you've not heard before. Whether it's part of your usual genre or not. Looking forward to seeing how this progresses! submitted by /u/Columbian_Toad [link] [comments]  ( 9 min )

Hi everyone. I am an artist. And programmer, and kind of a bit of everything. But what is important, is that I was an artist before the current "generative AI" was a thing, and I have been drawing, digitally and traditionally alike for like... a decade? Art, to me, is getting what is inside your head, and presenting it to others outside of your consciousness and thoughts. It's showing the world a piece of your interpretation, your experience, your impressions of the world you inhabit. It's about communicating to others your emotions, your ideas, your thoughts and feelings. Not everyone can draw, or paint, or sculpt. I could say "learn it, it's easy", but that would be a lie. It isn't easy. It is years upon years of constant, hard work, requiring focus and dedication, and a passion for l…  ( 11 min )

submitted by /u/DarronFeldstein [link] [comments]  ( 8 min )

How do I make AI-generated videos with prompts for free? submitted by /u/DankDude6T9 [link] [comments]  ( 8 min )

Hello AI enthusiasts! I'm a software engineer passionate about AI, and recently I've been experimenting with making my first AI game. In the game, you try to negotiate a price down on a watch with an AI-driven salesman, rewarding -or roasting lol- you depending on your bargaining skills. I’d be more than happy to get your thoughts and feedback on this idea, it's the first application I've built using AI so any tips would be much appreciated! Thanks! submitted by /u/gavo_gavo [link] [comments]  ( 9 min )

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I want to start making a YouTube channel, because I've got a passion project I want to work on with a Minecraft modpack. Obviously, Minecraft is a HUGE game and has thousands of videos posted every day... This is why I want to know if there is an AI that can rate videos based on editing, audience engagement, sound, etc... Also giving areas of improvement and the strengths of the video. Probably a big ask and SO far fetched, but there's always a chance of something being out there. submitted by /u/Columbian_Toad [link] [comments]  ( 9 min )

Can someone recommend a great AI photo editor that can take 100 profile photos and standardise them, IE crop so head is same size across all photos, background removed and placed on standard back ground. submitted by /u/Woodger [link] [comments]  ( 8 min )

​ https://preview.redd.it/9epla7xjdugb1.png?width=960&format=png&auto=webp&s=92190970027b08476ac9899a42d7099fe67cf5aa submitted by /u/Maxie445 [link] [comments]  ( 8 min )

Data analytics company Qureight has entered into a multi-year strategic research collaboration with AstraZeneca that will use AI models to accelerate research into lung diseases.[1] Zoom’s terms of service update establishes the video platform’s right to use some customer data for training its AI models.[2] Cigna, one of the country’s largest health insurance companies, faces a class action lawsuit over charges that it illegally used an AI algorithm to deny hundreds of thousands of claims without a physician’s review.[3] Japan plans guidelines for AI-savvy human resources.[4] Sources: [1] https://www.digitalhealth.net/2023/08/qureight-collaborates-with-astrazeneca-for-ai-lung-disease-research/ [2] https://www.cnbc.com/2023/08/07/zoom-ai-tools-trained-using-some-customer-data.html [3] https://www.medicaleconomics.com/view/cigna-using-ai-to-reject-claims-lawsuit-charges [4] https://asianews.network/japan-plans-guidelines-for-ai-savvy-human-resources/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

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Posted by Fuzhao Xue, Research Intern, and Mostafa Dehghani, Research Scientist, Google Adaptive computation refers to the ability of a machine learning system to adjust its behavior in response to changes in the environment. While conventional neural networks have a fixed function and computation capacity, i.e., they spend the same number of FLOPs for processing different inputs, a model with adaptive and dynamic computation modulates the computational budget it dedicates to processing each input, depending on the complexity of the input. Adaptive computation in neural networks is appealing for two key reasons. First, the mechanism that introduces adaptivity provides an inductive bias that can play a key role in solving some challenging tasks. For instance, enabling different num…  ( 93 min )

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​ https://preview.redd.it/xdp3bkwwpvgb1.jpg?width=2800&format=pjpg&auto=webp&s=513a63ed81eec85e6bc254f84e4208094afc7d4a Very interesting blog post from OpenCV.ai team about how can explore ChatGPT to serve for code development debugging. Introduction from the article: As programmers, we often work with familiar development environments, but occasionally we encounter new tools that can be time-consuming and challenging to learn. In such situations, having virtual assistance can be extremely beneficial. In this article, I will share my experience of contributing to OpenCV, a renowned open-source library, despite having limited knowledge of C++ and understanding its architecture. I achieved this with the assistance of ChatGPT, a Large Language Model (LLM). I hope you can find it interesting. More details are here. submitted by /u/No-Independence5880 [link] [comments]  ( 9 min )

submitted by /u/ABDULKADER90H [link] [comments]  ( 8 min )

I want to study Reinforcement Learning, but the concepts are really hard and mathematical. Whenever I think I grasp something I forget it the next day completly. The Basic Concepts of MDP is the only thing which I think I understood. But I cant understand the Training algorithms like Sarsa or Q-Learning and DQN and their implementations. I am really frustrated and overwhelmed. Does anyone know some good resources to understand the concepts and implementations of RL? submitted by /u/Menium [link] [comments]  ( 9 min )

In Q-learning (image), episodes are run, in the sense that, the states are visited in the order they appear as part of one sequence in an episode. In Dyna-Q (image) (which is btw described to be the same as Q-learning when the planning portion is deleted), there doesn't seem to be any iteration over the states of an episode. It just picks a state, applies the e-greedy policy on it to choose the action, learns, updates the model, then plans. Would Q-learning also work fine if we got rid of the "episodes" and just picked isolated state-action pairs? Thank you submitted by /u/AstronautVarious3791 [link] [comments]  ( 9 min )

I know for image recognition there is a rough intuition that neural network lower layers learn low level features like edges, and the higher layers learn more complex compositions of the lower layer features. Is there a similar intuition about what a value network or policy network learns in deep RL? If there are any papers that investigate this that would be helpful submitted by /u/Turkeydunk [link] [comments]  ( 9 min )

This post will discuss two scripting languages, but that’s not what the post is really about. It’s really about expressiveness and (or versus) productivity. *** I was excited to discover the awk programming language sometime in college because I had not used a scripting language before. Compared to C, awk was high-level luxury. Then a […] Productive constraints first appeared on John D. Cook.  ( 6 min )

A Möbius transformation is a function of the form where ad – bc = 1. We usually think of z as a complex number, but it doesn’t have to be. We could define Möbius transformations in any context where we can multiply, add, and divide, i.e. over any field. In particular, we could work over […] Möbius transformations over a finite field first appeared on John D. Cook.  ( 6 min )

Announcements Top Stories In-Depth The post DSC Weekly 8 August 2023 appeared first on Data Science Central.  ( 20 min )

Prompt engineers are emerging as key players in the development and optimization of AI models as artificial intelligence (AI) continues its evolution and becomes an integral part of various industries. As experts at crafting effective prompts, they have been instrumental in shaping the future of artificial intelligence through their ability to enable models to deliver… Read More »The emergence of prompt engineers: The next in-demand role in AI The post The emergence of prompt engineers: The next in-demand role in AI appeared first on Data Science Central.  ( 22 min )

As generative AI continues to sweep an increasingly digital, hyperconnected world, NVIDIA founder and CEO Jensen Huang made a thunderous return to SIGGRAPH, the world’s premier computer graphics conference. “The generative AI era is upon us, the iPhone moment if you will,” Huang told an audience of thousands Tuesday during an in-person special address in Read article >  ( 9 min )

Machine learning helped Waseem Alshikh plow through textbooks in college. Now he’s putting generative AI to work, creating content for hundreds of companies. Born and raised in Syria, Alshikh spoke no English, but he was fluent in software, a talent that served him well when he arrived at college in Lebanon. “The first day they Read article >  ( 6 min )

Organizations across industries are using extended reality (XR) to redesign workflows and boost productivity, whether for immersive training or collaborative design reviews. With the growing use of all-in-one (AIO) headsets, more teams have adopted and integrated XR. While easing XR use, AIO headsets have modest compute and rendering power that can limit the graphics quality Read article >  ( 6 min )

Professionals, teams, creators and others can tap into the power of AI to create high-quality audio and video effects — even using standard microphones and webcams — with the help of NVIDIA Maxine. The suite of GPU-accelerated software development kits and cloud-native microservices lets users deploy AI features that enhance audio, video and augmented-reality effects Read article >  ( 8 min )

AI and accelerated computing were in the spotlight at SIGGRAPH — the world’s largest gathering of computer graphics experts — as NVIDIA founder and CEO Jensen Huang announced during his keynote address updates to NVIDIA Omniverse, a platform for building and connecting 3D tools and applications, as well as acceleration for Universal Scene Description (known as OpenUSD), the open and extensible ecosystem for 3D worlds.  ( 10 min )

Picture this: Creators can quickly create and customize 3D scene backgrounds with the help of generative AI, thanks to cutting-edge tools from Shutterstock. The visual-content provider is building services using NVIDIA Picasso — a cloud-based foundry for developing generative AI models for visual design. The work incorporates Picasso’s latest feature — announced today during NVIDIA Read article >  ( 6 min )

NVIDIA researchers are taking the stage at SIGGRAPH, the world’s largest computer graphics conference, to demonstrate a generative AI workflow that helps artists rapidly create and iterate on materials for 3D scenes. The research demo, which will be presented today at the show’s Real-Time Live event, showcases how artists can use text or image prompts Read article >  ( 6 min )

DENZA, the luxury EV brand joint venture between BYD and Mercedes-Benz, has collaborated with marketing and communications giant WPP and NVIDIA Omniverse Cloud to build and deploy its next generation of car configurators, NVIDIA founder and CEO Jensen Huang announced at SIGGRAPH. WPP is using Omniverse Cloud — a platform for developing, deploying and managing Read article >  ( 5 min )

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you […]  ( 7 min )

Artificial intelligence (AI) adoption is accelerating across industries and use cases. Recent scientific breakthroughs in deep learning (DL), large language models (LLMs), and generative AI is allowing customers to use advanced state-of-the-art solutions with almost human-like performance. These complex models often require hardware acceleration because it enables not only faster training but also faster inference […]  ( 13 min )

Here's the pitch: We made an editor called Gamut that lets you enter your ideas in any form you want. Bullets, carefully constructed paragraphs, it doesn’t matter. Then, our patent-pending technology lets you convert to prose and adjust, shaping the text like a graphic designer shapes an image. We want r/MachineLearning's advice and field experience, because tbh we're just a bunch of teenagers who haven't even gone to college yet. Check it out: gamut.ink submitted by /u/gamut_ink [link] [comments]  ( 9 min )

I was just watching the documentary about the 'greatest movie never made', director Alejandro Jodorowsky's vision of Frank Herbert's Dune. There is a huge book that contains a storyboard version of the movie with lots of production art by artists Moebius, Chris Foss and HR Giger. The movie was to star Jodorowsky's son as Paul Atriedes, Salvadore Dali as the Emperor, Orson Wells as Baron Harkonnen and Mick Jagger as Feyd. Could one of today's AIs be 'fed' Jodorowsky's book and create a movie of his vision? Curious to know what your opinions are on this. Thanks. submitted by /u/shopdog [link] [comments]  ( 9 min )

Hi, all, I would like to use batch reaction trend data to build a regression model. I'm wondering what is the best way to approach this. Here's some background: Reaction Data: Time (min) Pressure (bar) Temperature (°C) Flow (kg/h) Gas Total (kg) 1 10 70 502 8 2 10.1 71 498 16 ... ... ... ... ... 102 10.3 76 475 850 Output: Polymer property X The reaction continues until a gas total is met and the time this takes depends on the other variables. I have ~700 batches of data in a format similar to the above and would like to predict polymer property X. As the variables can change minute to minute I was thinking of binning the variables into 5 minute bins using the mean and using these as variables for linear regression or similar. Is this a valid approach or is there another way I can approach the problem? Thanks! submitted by /u/Nefarious_P_I_G [link] [comments]  ( 9 min )

Hi everyone, I have put together a repo that provides comprehensive resources for Out-of-distribution Detection, Robustness, and Generalization. The repo contains articles, talks, libraries, papers, etc. Check it out. https://github.com/continuousml/Awesome-Out-Of-Distribution-Detection submitted by /u/Ok-Kaleidoscope-505 [link] [comments]  ( 8 min )

Alexander Amini, a Postdoctoral Associate at MIT, well known for the MIT's Introduction to Deep Learning Course, published a git repo called CAPSA for uncertainty prediction. This was introduced during the online course. The code was released under Thermis AI, Inc, a private company. He is the co-founder and CSO of the company. You can check how well the code was documented in the wayback machine. Recently, they removed the code base from the github and launched a pro version with selected companies as beta. The original repo (now called capsa-lite) was a great learning tool that I wanted to use. This was a quick way to try out different methods of uncertainty prediction using minimal code. Unfortunately, they have pulled all previous version of the code from the github repo. I was wondering if anyone knows a similar python package or has the old repo - would be really helpful! submitted by /u/shikamaru_77 [link] [comments]  ( 9 min )

I'm putting together an ML workstation primarily focused at handling CNN and Transformer workloads. Component selection so far: https://de.pcpartpicker.com/list/zVPtt7 I've got a couple of questions specifically regarding the motherboard. One concern I have is whether the space between the two GPUs is sufficient, as I'm planning to set them up using NVLink. Additionally, I'm curious about the compatibility of the case and motherboard for effective air cooling ( not considering water cooling at the moment). Anyone else with dual 3090s who can give some insights on how they've managed temperatures and potential overheating issues? Lastly, would upgrading to a Ryzen 9 5900X prevent me from bottlenecking the GPU's? Would love to hear your feedback and suggestions! submitted by /u/Hugejiji [link] [comments]  ( 9 min )

i want to fine tune any open source llm for code generation purpose with some of my code. any idea what model would be suitable? and any example of implementation? submitted by /u/learner_beginner [link] [comments]  ( 8 min )

Anyone here know what the trends are towards hiring for an AI/ML position without a PhD? Is it advisable to get a PhD if you want to be in the field and keep rising within it? submitted by /u/CleanGarden7051 [link] [comments]  ( 8 min )

Hi, I'm about to graduate in Physics (PhD). I am an experimentalist with a background in electromagnetic. I am trying to apply for jobs, but there are some few options for physicists (based on my geography). So, I am trying to learn some new skill for my future job. One option would be Machine Learning, which is on-demand and the field is growing. The other option is Quantum Computing. I can start a postdoc in quantum information theory as well. Each path, has its pros and cons, and the final decision is based on many factors. I just don't have enough data and information to say which one is more secure in the future? Which one has less compete? And also, is it possible to get hired without any serious project in ML, and just self-taught? If you were me, which one would you pick? Thanks submitted by /u/Jaded-Membership-602 [link] [comments]  ( 9 min )

What is a typical non-academic ML salary with a PhD... ... immediately after completing the PhD? (Assuming no academic positions ever post PhD.) ... after 10 years of experience? ... in biotech specifically? (More, less, or the same as average?) submitted by /u/Practical_Tea_3779 [link] [comments]  ( 8 min )

Hi all, I'm looking apply to some top masters for machine learning in the UK, so I'm guessing you know which one I'm referring to. I got some guidance from the application advisor, which state they like to look at the transcript the most to have an idea of my linear algebra, calculus and statistics ability. I got 70% in "Maths for Computer Science" and some other modules I strong first and 2:1 in some others, but in general my course wasn't too mathematically intensive. I did BSc Computer Science. I have been working as SWE the past 3 years. I have completed the following specialisation "Mathematics for Machine Learning and Data Science Specialization" and read "Mathematics for Machine Learning", as learning about mathematics actually got me into ML. I have also covered the videos on 3Blue1Brown etc. The application advisor said that certs don't really mean too much which is understandable. I can't change the past in terms of BSc transcript, therefore I was thinking a project may be a good way to showcase this. Any tips on how to best showcase this or get across my ability would be extremely helpful? submitted by /u/DNOFHF [link] [comments]  ( 9 min )

Hey, I've been trying to build an ML workstation and was considering the idea of using two RTX 3090's to get the extra VRAM instead of a single 4090. However, I've come across some confusion regarding whether they can share their VRAM or not. Do I need to run them via NVLink to achieve this? I believe PyTorch's data parallelism splits the batches across both GPUs, but that wouldn't effectively combine their VRAM right? Any advice or insights you can share on the topic would be highly appreciated! submitted by /u/Hugejiji [link] [comments]  ( 9 min )

I'm considering getting a graduate degree in ML. However, I am not very interested in NLP or academic research. I would like to learn things that are relevant to the intersection of ML and genomics or medicine. Are there any graduate programs/degrees to this effect? If so, which ones? submitted by /u/Practical_Tea_3779 [link] [comments]  ( 8 min )

Hi folks. I am sure you know the running gags around “thin OpenAI wrapper” products. Instead of more toy products, I am doing an experiment with some “AI engineering” to come up with a solution that’s closer to being usable in actual production cases. My background is in project management and data engineering, and I’ve built large systems for big companies and worked as a consultant in the space. I’ve seen enough crappy data pipelines for a lifetime. Hence. I want to do something different: A thin AI wrapper is not sufficient for having reliable data pipelines that use OpenAI for schema management and inference So this leaves me with the following doubts: ​ How to scale code horizontally and vertically? Using third-party solutions? SNS/SQS/Kafka? How to log and trace? Langsmith? Custom solutions? How to extend reliably with my own data, and make it stateful? Looking for your perspective ​ What do you think about the state of data engineering, MLOps, and infrastructure in AI companies? What do you think about how to scale properly the systems and prepare them for the future? In this code here, I do process some PDFs as a simple pipeline, what approaches do you think could be better? My current thinking and the state of the project ​ I should create a formal scale of usability. I am looking for your input here. I should improve model consistency, extends the model with custom domain knowledge, and make an early attempt to build simple user agents in the domain What I have is a schema inference, contracting basics, and a way to structure unstructured data I’m about to create a memory component that manages the data stored in vector dbs, as a DWH for AI If I bring this use case that was not something available easily to the public before, how best do it? Links: If you like my project, please give it a star :) my git repo submitted by /u/Snoo-bedooo [link] [comments]  ( 9 min )

Hey there, I got 2x 4090 RTX with 24GB GDDR each.I often ran into the problem of CUDA out of memory. Tried to allocate X MiB (GPU 0; 23.65 GiB total capacity; 22.75 GiB already allocated; 96.81 MiB free; 22.76 GiB reserved in total by PyTorch) I wonder if there is a way to take usage of both GPUs so the model is split onto both GPUs. When training models I use torch.nn.DataParallel to use both GPUs, but it seems like I am not doing it right for load the model. Can anyone help me? Both GPUs are available in the system - this has already been checked. submitted by /u/Sensitive_Limit1620 [link] [comments]  ( 9 min )

Hey folks, We're looking for people to join our research group. We are passionate about fine-tuning LLMs for downstream tasks, specifically LLAMA for imitating chat behaviour (being constraint aware). ​ The end goal is to build an open source app where you can clone and upload your chat history (say from Whatsapp) and it starts to answer like you Do let me know if it sounds interesting and you'd like to join us... https://preview.redd.it/sdo42mx1yogb1.png?width=1280&format=png&auto=webp&s=9de5008ed8ed18cedb25034d68984cb11e2a6a12 submitted by /u/im_datta0 [link] [comments]  ( 9 min )

humanscript is an inferpreter. A script interpreter that infers commands from natural language using AI. There is no predefined syntax, humanscripts just say what they want to happen, and when you execute them, it happens. https://github.com/lukechilds/humanscript This is a humanscript called tidy-screenshots. It takes an unorganised directory of screenshots and organises them into directories based on the month the screenshot was taken.It can be executed like any other script. https://preview.redd.it/2b0oz2kgwogb1.png?width=1576&format=png&auto=webp&s=9285805a1d0668ae5fe300857f9b67161b8ecda4 The LLM inferpreted the humanscript into the following bash script at runtime. ​ https://preview.redd.it/x8hwdrzhwogb1.png?width=2188&format=png&auto=webp&s=5fcba87a9606a446d169e8ae37b5c8c251525e5e The code is streamed out of the LLM during inferpretation and executed line by line so execution is not blocked waiting for inference to finish. The generated code is cached on first run and will be executed instantly on subsequent runs, bypassing the need for reinferpretation. ​ https://i.redd.it/t6b1stbkwogb1.gif The humanscript inferpreter supports a wide range of LLM backends. It can be used with cloud hosted LLMs like OpenAI's GTP-3.5 and GPT-4 or locally running open source LLMs like Llama 2. You can run humanscript in a sandboxed Docker environment with a single command if you want to have a play. https://github.com/lukechilds/humanscript#install-humanscript submitted by /u/dyslexiccoder [link] [comments]  ( 9 min )

lets say i have a set of images which contains sentences of text on it. now i want to generative images using some generative model with valid (meaningful) text in them. what i assume is just using gan or more powerful diffusion to generate images but i don't think the generated images won't contains valid text in them. i want the model to implicitly learn the text in the images without feeding external text or ocr on them. does any one know any paper trying to tackle this problem. any comments on this by anyone. submitted by /u/specializedboy [link] [comments]  ( 9 min )

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Hey folks, For the past 2 years I've been working on a library to automate the most tedious part of my own work - data loading, normalisation, typing, schema creation, retries, schema inference, evolution & ddl generation, self deployment.. Basically, as you build better and better pipelines you will want more and more, and dlt supports those options. The value proposition of this library is to automate the tedious work you do, so you can focus on better things. What's special about dlt? In the easiest form, you shoot response.json() json at a function and it auto manages the typing normalisation and loading, kind of like a pandas df.to_sql() but with auto schema inference, versioning and evolution. It supports loading to files, databases, and soon table formats and vector dbs. In its most complex form, you can do almost anything you can want, from memory management, microbatching, multithreading, extraction DAGs, 1 line Airflow/git actions deployment, dbt runner, streamlit app for data discovery, sql client, atomic state dictionaries, etc. The library is in use with early adopters, and we are now working on expanding our feature set to accommodate the larger community. We are adding Athena + Iceberg and Weaviate vector dbs next. Free forever The library is open source and will forever be open source. We will not gate any features for the sake of monetisation - instead we will take a more kafka/confluent approach where the eventual paid offering would be supportive not competing. Call for Feedback! Feedback is very welcome and so are requests for features or destinations. I would particularly love to hear from you: What destinations are you looking for from such a tool? And what use cases do you usually have? I'm a data engineer so my knowledge is more around loading external sources to a common space. Links Colab demos: Load to duckdb with schema evolution Docs main page Thank you in advance for your feedback! submitted by /u/Thinker_Assignment [link] [comments]  ( 9 min )

https://textadventure.v5games.com/ Hi All, I created these Text Adventure Games with AI, some help from the community which designs prompts+some avatars. The AI Characters can be created with an AI Art Generator. Voices and Illustrations are done using AI https://textadventure.v5games.com/ Let me know what you think! submitted by /u/BoxOrigi [link] [comments]  ( 8 min )

Staying on top of all changes, tools, and best practices with AI is getting increasingly hard. Each week I find just 1 piece of information that is most interesting across research, products, business news, and many more. No fluff guaranteed. Sharing the top research from this week's edition: https://preview.redd.it/fa3b1u39nlgb1.png?width=591&format=png&auto=webp&s=1ccd78136e3578396878fd9641605845f0309865 Summary: Meta released their latest open-source model, Llama 2, in partnership with Microsoft’s Azure platform. But Microsoft also offers OpenAI models and is a major investor in the company (they paid $14B for 49%). So, confused Matt asks, why would Microsoft partner with Meta, when it might undermine their investment in OpenAI? 💡 Answering the question: Spreading the risk: OpenAI may have the first mover advantages, but this does not always last (e.g. Blackberry, Myspace, Yahoo). Microsoft is betting on AI but keeps the chips diversified on multiple players. It’s beside the point: regardless of who Microsoft supports, their game is to attract all AI utilization on Azure. It's not about the tools but about the CPU/GPU cycles they can charge for. smart! The real AI gangsta: Microsoft is sitting on the holy trinity of AI now. Exclusive partnerships with top LLMs (OpenAI, Meta) Priority access to Nvidia GPUs And strategic assets like GitHub and Azure View tweet If you'd like weekly recaps like this sent to your inbox, consider subscribing to the Tomorrow Now newsletter. 😄 submitted by /u/TomorrowNowTech [link] [comments]  ( 9 min )

Microsoft Azure users can now turn to the latest NVIDIA accelerated computing technology to train and deploy their generative AI applications. Available today, the Microsoft Azure ND H100 v5 VMs using NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking — enables scaling generative AI, high performance computing (HPC) and other applications with a Read article >  ( 5 min )

I want an app that can draw a widemouth bass smoking a blunt. All the free ones ive tried give me supid anime girls when all I want is fish submitted by /u/Barefoot_slinger [link] [comments]  ( 8 min )

I’m interested in trying out a subscription-based generative AI service. Candidates include (but are not limited to) CoPilot, ChatGPT pro (or whatever it’s called), and Midjourney. Which generative service do you think is most worth the cost? submitted by /u/galactictock [link] [comments]  ( 8 min )

Is there any Free AI Voice Cloner for free, that allow me simply to install the Exe? And Has option to input my Voice to it that I record? I dont have any coding and command skills. so is there something simple to install? Thanks for Answers submitted by /u/Matejsteinhauser14 [link] [comments]  ( 8 min )

I’ve used several AI programs that work excellent on blurred/pixelated photos of human faces but beyond that, I have not had success finding a program that can render animals in similar way. I’m more looking for something that can make the quality of a pixelated photo of say, a dog, non pixelated. Or at least, much less pixelated. The images I’m trying to use are just absolutely horrible and not fixable, or I am just not using the best programs for my purposes. Or the programs I’m looking for simply do not exist yet. If you have any recommendations (Paid or free programs) please do share! I have a MacBook and an iPhone if that helps. Thank you! 💕 submitted by /u/briannaleidy [link] [comments]  ( 9 min )

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Hello, im in need of an ai that could rewrite for example a pdf document changing the wording but keeping the meaning of the content. Right now im a user of chatgpt plus, and trying to use code interpreter for that, ive managed to get what i want, but it isnt capable of rewriting more than two pages without crashing or simply stoping the process without any warning. I do not know if im using the prompting in a wrong way, any help would be apreciated, also, in case theres an ai out there capable of doing this in a better way id be glad to know about it. Thank you guys. submitted by /u/namelessgang [link] [comments]  ( 9 min )

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"It looks likethisis some sortof programming syntax maybe JavaScript perhaps? Let metell ya though buddy dat aintmuch informatio todo wit. Wouldya care ta tell mesomewhat ye wanna know boot heck, might make things easier ferus botsto give yo useful responses faster innasecondsoffuture interactions brotha man :)" Anyone else used this site? It's through a site called nimblebox.ai, they have different models and allow you to adjust the temperature submitted by /u/jordan_jpg [link] [comments]  ( 8 min )

submitted by /u/wolfdeathkill [link] [comments]  ( 8 min )

Don’t believe everything you hear in the media… I learned this firsthand. This one time I accidentally went on Jessie Waters…for real 😂 https://youtu.be/1X31DHV0gyg?si=fU8p2D4-ShTWUdQs https://open.spotify.com/episode/1M6dbrrP4EoudfTUvD4BqF?si=YMBCFXYfTsmUeOXAe_-lMg submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

I have these chiptune songs I made myself, and I want to know if there is any AI that can remaster them with real instruments, etc., like an old 8-bit video game song that is updated to a modern version in a remake. Is any already AI capable of doing that? submitted by /u/Severo_ [link] [comments]  ( 8 min )

A common idiom in command line processing of text files is ... | sort | uniq | ... Some process produces lines of text. You want to pipe that text through sort to sort the lines in alphabetical order, then pass it to uniq to filter out all but the unique lines. The uniq utility […] Sort and remove duplicates first appeared on John D. Cook.  ( 5 min )

Fair Data Forecast Interview with Gregor Stühler of Scoutbee Scoutbee’s CEO and founder, Gregor Stühler, who has a background in computer science and  electrical engineering, first learned about the challenges of procurement and supply base management as a project engineer for a multinational medical device company. Scoutbee’s focus on solving supply base problems through hybrid… Read More »Scaling Supply Base Data and Reuse with Knowledge Graphs and LLMs The post Scaling Supply Base Data and Reuse with Knowledge Graphs and LLMs appeared first on Data Science Central.  ( 19 min )

The video gaming industry has an estimated user base of over 3 billion worldwide1. It consists of massive amounts of players virtually interacting with each other every single day. Unfortunately, as in the real world, not all players communicate appropriately and respectfully. In an effort to create and maintain a socially responsible gaming environment, AWS […]  ( 13 min )

Predictions from the OncoNPC model could enable doctors to choose targeted treatments for difficult-to-treat tumors.  ( 9 min )

Hello everyone! I'm a newbie and making my project on machine learning and the aim is create a programme to recognise a spice by feeding some chemical constituents, but I can't find appropriate dataset for it. I have been searching for months, and now I'm a bit desperate, so I'm asking anyone interested for help... I know maybe it was a mistake to choose exactly this topic, but I can't drop the project. submitted by /u/Acceptable-Muscle-98 [link] [comments]  ( 8 min )

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Hello I am attempting to reduce a matrix that is 57 by 256 to 57 to 128. I was attempting to use PCA but it failed as maximum size would be 57 by 57. I was also attempting an autoencoder but the syntax behind this is very confusing so If anyone could give me adivce that would be great. Thank you submitted by /u/amayorgafcw [link] [comments]  ( 8 min )

Hello, I have been working on an OpenAI-compatible API for serving LLAMA-2 models written entirely in Rust. It supports offloading computation to Nvidia GPU and Metal acceleration for GGML models ! Here is the project link: Cria- Local LLAMA2 API You can use it as an OpenAI replacement (check out the included `Langchain` example in the project). This is an ongoing project, I have implemented the `embeddings` and `completions` routes. The `chat-completion` route will be here very soon! Really interested in your feedback and I would welcome any help :) ! ​ ​ submitted by /u/amindiro [link] [comments]  ( 9 min )

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Open source Audio pipeline for transcription, translation and summarization. Check out our demo page to generate your own transcription, summary, and translation, or use our browser extension to get live transcriptions. submitted by /u/eusben [link] [comments]  ( 8 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

Hey! I am working on a project to predict the best sequences of marketing channel so that sales is maximized. I have 20 ways of reaching out to the customer (email, phone, face2face...). I have 20 days of interaction history and it's generated sales, recorded for past 2 years. I have to predict for the next 20 working days(1 month) So far, I have tried ensemble methods, svm, fully connected nn, etc. But it is quite apparent that these are not good solutions. Any suggestions on ml/dl methods? Papers, blogs or other resources would be much appreciated submitted by /u/TUSH11235 [link] [comments]  ( 9 min )

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Greetings fellow researchers, I am 27, currently working remotely at a healthcare IT company based in Silicon Valley (6+ years in industrial research) where I apply deep learning methods and large language models. I recently received an exciting opportunity to pursue a PhD at the Technical University of Denmark (DTU) in a similar research area. While I am grateful for my current position and compensation, Have published in NeurIPS, EMNLP, ACL, ACM etc (NLP) with really good citations under company. I feel unsatisfied with the learning opportunities available in company & industry. I am strongly considering pursuing the DTU PhD program full-time, but wanted to get perspectives from others before making a decision. How strong is DTU's AI research community? Given the rapid advances in large language models, is now an ideal time to immerse myself in academic research? There are many topics that interest me, including fairness, ethics, hallucinations, quantization, specialized domains like healthcare/finance, and federated learning combined with LLMs. Would appreciate any insights on whether moving into academia would be a wise choice at this stage versus remaining in industry. I welcome any suggestions or considerations I should keep in mind. Thank you for taking the time to share your thoughts! submitted by /u/Traditional-Poet2746 [link] [comments]  ( 9 min )

Context I'm working on an RNN-based model that should learn how to guess the next character given a simple prompt based on all scripts from Friends to generate non-existing Friends dialogue. It is heavily inspired by Andrej Karpathy's blog post on RNN's. I'm mostly doing this for training, and because it's pretty fun. I have a little experience with deep learning in the sense that I am familiar with most common architectures and have intermediate understanding of how deep learning models work and are trained. I haven't created many models from scratch though, yet. Network My GRU is fairly simple. I'll save you the exact code, but instead give a systematic overview of all network layers. It's implemented with Pytorch: INPUT: sequence of integers representing a symbol based on mapping e…  ( 9 min )

submitted by /u/Better-Process5239 [link] [comments]  ( 8 min )

Hey. I'm currently working on an application that digitalizes text from physical book pages using Google's Cloud Vision API. I'm looking to add a functionality that can recognize and highlight underlined words within the scanned pages. I initially thought this would be a common feature and expected to find existing open-source solutions or libraries that I could use. To my surprise, I've been unable to find any. I am just really bad at finding it, or is this not as straightforward as I initially thought? submitted by /u/pangu2 [link] [comments]  ( 8 min )

Dear all, Here is Computer Vision News of August 2023. Read 44 pages about AI, Deep Learning, Computer Vision and more! Online version (recommended) PDF version Free subscription on page 44. Enjoy! https://preview.redd.it/e143wha20ggb1.jpg?width=794&format=pjpg&auto=webp&s=14a699f80f4b2de94addc8242e8978d3e185309f submitted by /u/Gletta [link] [comments]  ( 8 min )

Experts, I am a beginner here and seeking some advise here please. I am have compiled a high quality Q&A dataset (around 1200 entries) for a domain specific topic. What's the best course of action here to use LLM with that specific knowledge base? 1) Finetuning a model? if so which one is a good candidate? OpenAI let's me finetune some models and later, all my users have to do is use pass the model name to the API 2) Use the regular vector database + embeddings for augmented retrieval I prefer (1) but I am not sure how it will perform. Option (2) should work, since we really just use semantic search to bring in context to the LLM, etc. I hope you can say that (1) works nicely, if not please help me learn why. Thank you in advance! submitted by /u/entered_apprentice [link] [comments]  ( 9 min )

Where to find LLM related pytorch code with code explanations? submitted by /u/thorin_olamadal [link] [comments]  ( 8 min )

First time submitter here and was unable to find a similar post (and thought the community might benefit from this in the future!). How do I withdraw from Neurips? All the instructions I found are from 2017, 2018. Do I need to contact someone or do I just need to "Add Withdrawal" on OpenReview. submitted by /u/Dramatic-Gap-4681 [link] [comments]  ( 8 min )

Just what the title says. Also is this (splitting prediction into 2 stages) a prominent paradigm in other areas of ML too? I am reading about something called the "Action Transformer" created by Adept AI, and it also has 2 stages: instruction generation and code generation. submitted by /u/FloatingDelusion [link] [comments]  ( 8 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

submitted by /u/CreepToCrypto [link] [comments]  ( 8 min )

Web dev student here and I'm interested in knowing more about creating products that actually use AI to help its users (not products that just use GPT in the backend). More specifically, I want to build a food supply management app for restaurants for my school thesis. This app will use AI to analyse food supplies and assign them purchase priority, value, and complexity scores (maybe just priority if it's too hard). Restaurant owners could then determine what foods should be purchased before others based on the priority scores. For example, a restaurant may only have 10 tomatoes left and the average usage of tomatoes in this restaurant is 12 per week. Based on this, a priority would be assigned to purchase x amount of tomatoes. Other factors that could be taken into account for the prior…  ( 10 min )

Hello everyone Basically i just need to post some text into one website everyday for my work The problem is there are many steps involves to post one data value, I was wondering if there is a tool that can learn my tasks and then post some of the data to the website from google sheets? I'm open to any suggestions and advice. Thanks in advance. ​ submitted by /u/Maxduel [link] [comments]  ( 8 min )

I want to convert a few books into audiobooks. Are there any AI options out there that are free and will give me something I can use offline? I typically listen to books on my phone while I'm out, so something like Edge browser isn't going to work. I've heard that there are some great options, but I've only seen some web paid services, and for my purpose, it's too expensive just to get an audiobook out of it. This is all just for personal use. submitted by /u/UUkiee [link] [comments]  ( 8 min )

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https://imgur.com/WR0Tny9 My agent needs to learn to take one action in my environment and there are only two possible actions that the agent can take at each time step. The state is just the time step, so every episode has 240 time steps and the agent just needs to learn to take one optimal action out of two possible actions for every time step. I have set this up as simply as I can as a starting point to make sure the algorithm is implemented correctly and that the agent can learn. I am using n-step expected SARSA. The bottom plot shows the count for how many times the agent took each action during each episode. The middle plot has the temporal difference error in blue and the "modelling error" in orange. The modelling error is the difference between the actual discounted return and the TD target for each time step, summed up for each episode. The red line is the return that the agent would get if it took the optimal action in every time step. 0.11, the blue line in the bottom plot, is the optimal action for the agent to take at every time step. The other action will never result in a reward other than 0. So it should be fairly simple for the agent to learn what action to take at every time step and it does learn that at the start. But then, as you can see in the top plot, the agent suddenly starts taking the non-optimal action more often after around episode 450. So I'm just wondering why that would happen. Why would the agent learn to take the optimal action at most time steps and then suddenly decide that it will start taking other actions? For more context, the learning rate is 0.6, n is 6, epsilon is decayed by 1/(n_episodes/1.1) every episode so it reaches 0 slightly before the final episode. Any ideas based on this information why the agent would decide to start taking the non-optimal action? Or any suggestions for how I could figure out why it would start taking the non-optimal action? submitted by /u/lifelifebalance [link] [comments]  ( 9 min )

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Does anyone know code implementations for TarMAC: Targeted Multi-Agent Communication? submitted by /u/tessherelurkingnow [link] [comments]  ( 8 min )

The previous post looked at the swish function and related activation functions for deep neural networks designed to address the “dying ReLU problem.” Unlike many activation functions, the function f(x) is not monotone but has a minimum near x0 = -1.2784. The exact location of the minimum is where W is the Lambert W function, […] Swish function and a Swiss mathematician first appeared on John D. Cook.  ( 5 min )

Swish, mish, and serf are neural net activation functions. The names are fun to say, but more importantly the functions have been shown to improve neural network performance by solving the “dying ReLU problem.” Softplus can also be used as an activation function, but our interest in softplus here is as part of the definition […] Swish, mish, and serf first appeared on John D. Cook.  ( 7 min )

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It’s incredible how many organizations utilize Generative AI (GenAI) and Large Language Models (LLMs) to enhance their information assembly, integration, and application abilities. These GenAI technologies have been applied in various areas, from drafting legal documents and resolving service issues to coding software applications and (er, um) writing blog posts. The potential uses of GenAI… Read More »Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part I The post Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part I appeared first on Data Science Central.  ( 23 min )

The movie that came out in the 80s is a great flick for it’s time. Do you guys think we will ever experience a sort of unstoppable super soldier when it comes to our police / swat forces ? We are replacing many jobs with robots. From surgery procedures in hospitals to flipping burgers. It’s not above the realm of possibility to think we may someday soon see a hybrid police force. What do you guys think ? submitted by /u/2bJavazon [link] [comments]  ( 8 min )

It's commonly used on tiktok for reddit narration story videos, here is an example: https://www.tiktok.com/@creekyadvice/video/7263509593488166186. Anyone have any idea? submitted by /u/DanielTube7 [link] [comments]  ( 8 min )

I am a linguist, translator, and copy editor looking to move my career into natural language processing instead. I have no computer science background. What would you suggest as some steps to take, both now and in the future, as I plan out my career? It looks like I am going to need to learn Python, but I'm not 100% sure, and there's so little established in such a new field. submitted by /u/StrangersWithAndi [link] [comments]  ( 8 min )

Interesting experiment i thought of. What if we gave AI access to web browser, and let it do whatever it wants? It could create accounts on any social media, email accounts, ad comments everywhere and such. of course, ai by itself does not have any agenda or need to do anything, so ai would need to be fed some kind of personality simulation first. Lets say ai was either fed personality based on extensive twitter or reddit history of someone's post. Using that, basic psychological traits, beliefs and maybe goals could be determined. Such ai would simulate person sitting in front of pc, so it would need to parse the content of webpages, but i don't think it would be that of a problem. And it would maybe also have access to some bank account with some money to maybe pay for online subscriptions and such. But who knows, maybe thanks to simulating someone's personality, it would attempt to donate money to some charity or lose it on onlyfans? submitted by /u/rogaldorn88888 [link] [comments]  ( 9 min )

I just published “Safe For Humans AI” – free to read online https://leanpub.com/safe-for-humans-AI/read Free to read online, and eBook versions released under a Creative Commons License (no commercial reuse, feel free to share). The full title of my short book is: Safe For Humans AI A "humans-first" approach to designing and building AI systems. submitted by /u/MWatson [link] [comments]  ( 8 min )

While some schools have curbed the use of generative AI, the University of Hong Kong (HKU) is going all in and urging both its teachers and students to embrace the technology. The University of Hong Kong is supporting this by giving teachers and students free access to various generative AI tools, including Microsoft Azure OpenAI and OpenAI’s ChatGPT and DALL-E.[1] Intel’s CEO, Pat Gelsinger, has called NVIDIA the clear market leader who has done a great job within the AI space.[2] AI powerhouse, OpenAI has released some new features for its sensational chatbot, ChatGPT. The new features allow the chatbot to show suggested follow-up prompts at the bottom of its responses. The new features were announced by the company via a tweet on its official Twitter handle.[3] Asian Americans and women in the workforce are the most concentrated in fields where AI could assist or replace their job tasks, according to new research.[4] BushAICave.com Sources: [1] https://www.zdnet.com/article/another-major-university-is-supporting-generative-ai-use-but-with-serious-guardrails/ [2] https://wccftech.com/intel-ceo-acknowledges-nvidia-as-ai-market-leader-says-they-have-done-a-good-job/ [3] https://indianexpress.com/article/technology/artificial-intelligence/chatgpt-gets-new-updates-heres-how-they-enhance-user-experience-8877847/ [4] https://www.nbcnews.com/news/asian-america/asian-american-workers-heavily-affected-ai-rcna98179 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

And if so, how has this account lasted 2 years on reddit? 🤔 submitted by /u/TheHeirOfElendil [link] [comments]  ( 8 min )

I’m a filmmaker and I’m just experimenting with AI. I just had fun crafting a film trailer to understand the today’s limits of these tools. I used Midjourney, Runway Gen-2, StableDiffusion, Premiere, After Effects. The movie it's called "The Phoenix", which hints at the film's underlying theme of rising from the ashes, symbolizing female empowerment, all wrapped in a bit of sarcastic humor from a male perspective. I'm sharing because I genuinely want to know what you guys think. Any and all thoughts are welcome. If you're curious about the workflow or the process behind the creation of this trailer, I'd be happy to share more. The Phoenix - She rises from the ashes submitted by /u/Lrnz_reddit [link] [comments]  ( 8 min )

Links in my bio for more content like this! submitted by /u/No_Understanding162 [link] [comments]  ( 8 min )

Hello there I'm curious what the user guidelines and restrictions are for the Adobe ai generative fill is and if there are possible better more higher quality and less restricted ones out there. submitted by /u/Team_Sonic_Gaming [link] [comments]  ( 8 min )

I'm not sure if this is the right subreddit to ask this on, but I'm creating this chatbot on dialogflow and I made the first intend, but I can't figure out how to enable it. whenever I test it, it shows the intend to be idf, and I can't just change the name of the intend to my current intend so it can recognize all the requests I've included in that intend. how do I do that? submitted by /u/penguinsandpandas00 [link] [comments]  ( 8 min )

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In principle you generate an RSA key by finding two large prime numbers, p and q, and computing n = pq. You could, for example, generate random numbers by rolling dice, then type the numbers into Mathematica to test each for primaility until you find a couple prime numbers of the right size. In practice […] Generating and inspecting an RSA private key first appeared on John D. Cook.  ( 6 min )

At its core, RSA encryption is modular exponentiation. That is, given a message m, the encrypted form of m is x = me mod n where e is a publicly known exponent and n is a product of two large primes. The number n is made public but only the holder of the private key […] RSA encryption in practice first appeared on John D. Cook.  ( 5 min )

A few days ago I wrote about using the CMU Pronouncing Dictionary to search for words that decode to certain numbers in the Major mnemonic system. You can find a brief description of the Major system in that post. As large as the CMU dictionary is, it did not contain words mapping to some three-digit […] Code to convert words to Major system numbers first appeared on John D. Cook.  ( 6 min )

Hello r/MachineLearning! I'm working on understanding how a neural network converges and wish to approach this mathematically. Can anyone recommend resources, papers, or tools that could assist me in proving this? Thank you in advance for your help! Edit: Removed converges faster to remove ambiguity submitted by /u/abystoma [link] [comments]  ( 8 min )

Do you aware of any work for realtime action recognition that use transformer? This is different with conventional transformer in a sense that we don’t have access to future information, so how do we change the training strategy? Also, it’s inefficient if we use the entire history; are there any smart way to select which frame in the past to keep? submitted by /u/Ok_Influence505 [link] [comments]  ( 8 min )

u/Vectara is now integrated with r/flowise, so you can easily build no-code GenAI Apps at scale. Check out the video here: https://twitter.com/ofermend/status/1687138158692196352 You can sign up for a free vectara.com account to get started. submitted by /u/ofermend [link] [comments]  ( 8 min )

I've recently started to explore the possibility of working with custom tokenizers. I will preface this by saying I'm not a tokenizer guy. I just don't know that much about their construction. I understand how they work, but I'm probably behind the latest developments in tokenizers. So, I thought it wisest to reach out to the community for advice or clarity. Context: I've collected about 15 GB of data over the last month. It's incredibly clean and well-organized. The core goal of the data is to train a model to solve or assist with a particular development problem. This means that much of my data is a code/natural language mix. It's delimited clearly, and the formatting is uniform. The entire dataset has been normalized and standardized. It's taken me a lot of time to produce and that's…  ( 10 min )

​ https://preview.redd.it/mbkb10icqbgb1.png?width=1400&format=png&auto=webp&s=7a15423060ddfeffe4651340bcc6fd7cf36dde10 I started a blog post series about the limitations of language models for dealing with long texts. Feedback is welcome! submitted by /u/JClub [link] [comments]  ( 8 min )

Hi everyone, With the recent boom of LLMs, we have seen both ends of the spectrum advance at a very fast pace, from OpenAI GPT4, which runs on huge data centers operated by Azure, to llama.cpp, which runs on consumer laptops. While both have their pros and cons, for instance, open-source models on decentralized compute reduce the need to trust or rely on centralized actors like Cloud providers, the efficiency of running training/inference on personal setups is not often discussed. I am therefore interested in learning how more energy/cost efficient it is to train/serve AI models on data centers vs doing it on personal computers. Do you know if there have been studies? In theory, I guess that several factors, such as economies of scale, use of renewable energy sources in some data centers, such as Canada, advanced cooling systems and advanced hardware, make data centers more cost/energy efficient. I guess some modeling on a precise use case where we fix some variables could help have an idea. For instance, one could ask, what is the energy/cost/time needed to predict 1 billion tokens from a Llama 2 70B in a data center with X amount of A100s, vs on Y different consumer CPU / GPUs. If anyone has references to models or past studies I would be quite interested. Of course, using data centers implies trusting those people, but I am not considering that factor for this discussion as I am focusing on understanding best what is the best setup to have optimal enrgy/cost/time for AI. submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

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As part of ICCV 2023 in Paris, this year we are organizing a challenge on solving domain gaps that occur when computer vision models are transferred across geographical locations. The challenge covers three tracks in unsupervised scene adaptation, image adaptation and universal adaptation. The challenge is open to everyone, with attractive prizes for the winners. Check it out at the following links! Challenge Rules and Guidelines: https://geonet-challenge.github.io/ICCV2023/challenge.html Challenge Registration: https://forms.gle/zSZA1iaPD3mZxjyn7 Code and baselines: https://github.com/ViLab-UCSD/GeoNet The training data for the challenge is already available, and the test data will be released to the registered participants. submitted by /u/GeoNetICCV2023 [link] [comments]  ( 8 min )

Hi guys Im looking for groups or communities where i could discuss about certain topic. Im a software developer and a mechanical engineer and recently made an app that combines gpt4 model to perform engineering tasks like CAD models creation and performing stress analysis. I would like find people who share the same passion and perhaps would like to discuss about that, exchange the concepts, ideas and visions. Im getting to the point where i will need to implement own trained model and im no ML expert so would be great to discuss about the architecture etc. Here is a demo of my app MechDesigner Assistant AI: Future Engineers Best regards Pyotr submitted by /u/pyotr_vozniak [link] [comments]  ( 9 min )

Hi, I am a new starter with ML apps and want to build a first app preferably using existing (trained) models. The idea is an app that takes a text description of a wished kick drum (for example: create a 808 kick with enhanced subs and filtered above 15kHz) and then generates a corresponding hifi sample of the description (44,1k or 48k). I would like to learn how to do that with some peers happy to help me. As said this would be my first attempt. About me: I only followed Deep Learning theoretical courses from Andrew Ng and never built or used existing models so I'd appreciate some guidance if you are interested to support. Thanks a lot submitted by /u/freeabt19 [link] [comments]  ( 9 min )

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Popular machine model techniques such as LightGBM and XGBoost output predictions that are unimodally distributed(only one hump) but seem to beat other models specialized to deal with multimodal data. Or am I just wrong? It just doesnt look right. https://preview.redd.it/6xrd7hgm4agb1.png?width=1000&format=png&auto=webp&s=a4518549f609c6436af410ae87a0c6a24cff6ea7 submitted by /u/runawaychicken [link] [comments]  ( 8 min )

Hey I've tried to implement the transformer architecture on my own to understand it better. The outputs look fine (I'm only looking at shapes) and I wanted to know if it's right firstly, and if there is anyway to implement it in a more efficient way. Code - import torch import torch.nn as nn class MultiHeadSelfAttention(nn.Module): def __init__(self, nheads=8, dim=512, bias=True, dropout=0.2): super().__init__() assert dim % nheads == 0, "dimension must be divisible by number of heads" self.nheads = nheads self.dim = dim self.head_dim = dim // nheads self.scale = self.head_dim**-0.5 self.softmax = nn.Softmax(dim=-1) self.dropout = nn.Dropout(dropout) self.to_keys = nn.Linear(dim, self.dim_heads * nheads, bias=bias) self.to_queries = nn.Linear(dim, self.dim_heads * nheads, bias=bias) self.to_values = nn.Linear(dim, self.dim_heads * nheads, bias=bias) self.to_out = nn.Linear(self.dim_heads * nheads, dim, bias=bias) def change_shape(self, x): b_size = x.shape[:-1] return x.reshape(*b_size, self.nheads, self.head_dim) def forward(self, x, mask=True): q = self.change_shape(self.to_queries(x)) k = self.change_shape(self.to_keys(x)) v = self.change_shape(self.to_values(x)) dot_score = q @ k.transpose(-2, -1) * self.scale if mask: tril = torch.tril(torch.ones(dot_score.shape[-2:])) dot_score = dot_score.masked_fill(tril == 0, float("-inf")) attn = self.softmax(attn) attn = self.dropout(attn) out = torch.einsum("bnk,bnd->bnd", attn, v) b_size = out.shape[:-2] out = out.view(*b_size, -1) return self.to_out(out) Thank you! submitted by /u/04RR [link] [comments]  ( 9 min )

https://github.com/NoteDancing/Note This project allows you to easily implement parallel training with the multiprocessing module. submitted by /u/NoteDancing [link] [comments]  ( 8 min )

In the forward process of diffusion models, gaussian noise is added -- when this is done, is the resulting "noisy image" clipped to be within the pixel-value bounds (ie [0, 255] or [0, 1]), or is it allowed to exceed these limits? Clipping makes sense as there is no interpretation for pixel values which exceed these limits. On the other hand, the problem with clipping is that if the added noise is clipped, you are not adding truly gaussian noise, which seems problematic as much of the theory behind diffusion models assumes true gaussian noise. Any ideas about what is done in practice, and whether or not this has implications from a theoretical standpoint? submitted by /u/alkaway [link] [comments]  ( 9 min )

Good Evening ML peeps So I am currently creating a dataset in a team of three. This dataset is aimed to create a object detection model for around 11 classes. We have aimed to label around approx. 4000. Our current workflow is a couple of scripts scraping from Pinterest and using Label Studio for labeling. We labeled approx. 25% to our goal but realized that we are about to burn out. We'd prefer that whatever solution there is is self hosted and not paid. Thoughts? is there some kind of workflow we are missing to create a dataset? submitted by /u/PlanetAcorn [link] [comments]  ( 9 min )

I am working on a project that is very popular with the inception of Langchain + GPT applications. However, I want to make it open source and hence don't want to use GPT. So something like Langchain + LLama2, etc. I know currently Langchain only supports GPT but any other ideas are highly appreciated! submitted by /u/vishank97 [link] [comments]  ( 8 min )

Hi all, I'm interested in redesigning my application to utilize an open-source embeddings model and a different vector DB. My current issue with embeddings is that processing large volumes of data into a vector DB using ada-002 is unreliable, with frequent API timeouts occurring or issues interacting with Pinecone. This is super problematic as it's difficult to track which data has / hasn't been stored correctly. I also know that many open-source embeddings models are more performant and will allow for more long term control over my data. However, the advantage of using OpenAI / Pinecone has of course been simplicity in production and not having to worry about queries / retrieval working efficiently. To give context, I'm dealing with a large volume of documents, such that if I were to embed my documents into a FAISS index with a small sentence transformers model, it would constitute 12GB, so a really simple solution like storing within the same application database is probably a no-go. In initiating this switch, I want to know the best approach towards: A) Utilizing an open-source embeddings model in a production context (is it best to host as an API via a cloud provider and what are some considerations I should think about? What's a fast / reliable way of setting this up? I would like prioritise a more simple approach if possible.) B) What Vector DB I should be looking into as an alternative and what's the best way to achieve self-hosted so that it would be equally performant compared to hosted services like pinecone (Docker? AWS?)? submitted by /u/theheffalump2000 [link] [comments]  ( 9 min )

SARSA is a TD algorithm for control (learning optimal policies). In the book it's written like this: image. The idea is to learn the action-value function instead of the value function for a policy that we keep improving (using GPI). Once we learn the converged action-value function for all states, the optimal policy is greedily derived from the action-value function (basically take the most promising action at each state). In contrast, TD for value estimation is written like this: image. Here we keep the policy fixed and just keep iterating over the multiple episodes, whilst refining the value estimate. My question is, why can't we just change TD for value estimation to just greedily update the policy at each stage? That would be in the spirit of generalized policy iteration (GPI) too. In other words, a version of SARSA which doesn't use action-value functions, but instead use value functions? submitted by /u/AstronautVarious3791 [link] [comments]  ( 9 min )

Clustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in applied science. This paper presents an unsupervised machine learning algorithm to extract defining characteristics of earthquake ground-motion spectra, also called latent features, to aid in ground-motion selection (GMS). In this context, a latent feature is a low-dimensional machine-discovered spectral characteristic learned through nonlinear relationships of a neural network autoencoder. Machine discovered latent features can be combined with traditionally defined intensity measures and clustering can be performed to select a representative subgroup from a large ground-motion suite. The objective of efficient GMS is to choose characteristic records representative of what the structure will probabilistically experience in its lifetime. Three examples are presented to validate this approach, including the use of synthetic and field recorded ground-motion datasets. The presented deep embedding clustering of ground-motion spectra has three main advantages: 1. defining characteristics the represent the sparse spectral content of ground-motions are discovered efficiently through training of the autoencoder, 2. domain knowledge is incorporated into the machine learning framework with conditional variables in the deep embedding scheme, and 3. method exhibits excellent performance when compared to a benchmark seismic hazard analysis.  ( 2 min )

(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic system models that are sufficiently accurate in all relevant state-space regions. These models must also be computationally cheap enough to ensure real-time tractability. Data-driven surrogate models for mechanistic models can be used to reduce the computational burden of (e)NMPC; however, such models are typically trained by system identification for maximum average prediction accuracy on simulation samples and perform suboptimally as part of actual (e)NMPC. We present a method for end-to-end reinforcement learning of dynamic surrogate models for optimal performance in (e)NMPC applications, resulting in predictive controllers that strike a favorable balance between control performance and computational demand. We validate our method on two applications derived from an established nonlinear continuous stirred-tank reactor model. We compare the controller performance to that of MPCs utilizing models trained by the prevailing maximum prediction accuracy paradigm, and model-free neural network controllers trained using reinforcement learning. We show that our method matches the performance of the model-free neural network controllers while consistently outperforming models derived from system identification. Additionally, we show that the MPC policies can react to changes in the control setting without retraining.  ( 2 min )

Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data. Till now, rehearsal-based methods, keeping a small part of data from old tasks as a memory buffer, have shown good performance in mitigating catastrophic forgetting for previously learned knowledge. However, most of these methods typically treat each new task equally, which may not adequately consider the relationship or similarity between old and new tasks. Furthermore, these methods commonly neglect sample importance in the continual training process and result in sub-optimal performance on certain tasks. To address this challenging problem, we propose Relational Experience Replay (RER), a bi-level learning framework, to adaptively tune task-wise relationships and sample importance within each task to achieve a better `stability' and `plasticity' trade-off. As such, the proposed method is capable of accumulating new knowledge while consolidating previously learned old knowledge during continual learning. Extensive experiments conducted on three publicly available datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) show that the proposed method can consistently improve the performance of all baselines and surpass current state-of-the-art methods.  ( 2 min )

Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs. However, previous methods usually overlook the issues in practical applications, i.e., the out-of-sample issue and the noise issue. To address the above issues, in this paper, we propose an effective and efficient UMGL method to explore both complementary and consistent information. To do this, our method employs multiple MLP encoders rather than graph convolutional network (GCN) to conduct representation learning with two constraints, i.e., preserving the local graph structure among nodes to handle the out-of-sample issue, and maximizing the correlation of multiple node representations to handle the noise issue. Comprehensive experiments demonstrate that our proposed method achieves superior effectiveness and efficiency over the comparison methods and effectively tackles those two issues. Code is available at https://github.com/LarryUESTC/CoCoMG.  ( 2 min )

We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models. On seven vision-language datasets, OpenFlamingo models average between 80 - 89% of corresponding Flamingo performance. This technical report describes our models, training data, hyperparameters, and evaluation suite. We share our models and code at https://github.com/mlfoundations/open_flamingo.  ( 2 min )

The development of technologies for causal inference with the privacy preservation of distributed data has attracted considerable attention in recent years. To address this issue, we propose a data collaboration quasi-experiment (DC-QE) that enables causal inference from distributed data with privacy preservation. In our method, first, local parties construct dimensionality-reduced intermediate representations from the private data. Second, they share intermediate representations, instead of private data for privacy preservation. Third, propensity scores were estimated from the shared intermediate representations. Finally, the treatment effects were estimated from propensity scores. Our method can reduce both random errors and biases, whereas existing methods can only reduce random errors in the estimation of treatment effects. Through numerical experiments on both artificial and real-world data, we confirmed that our method can lead to better estimation results than individual analyses. Dimensionality-reduction loses some of the information in the private data and causes performance degradation. However, we observed that in the experiments, sharing intermediate representations with many parties to resolve the lack of subjects and covariates, our method improved performance enough to overcome the degradation caused by dimensionality-reduction. With the spread of our method, intermediate representations can be published as open data to help researchers find causalities and accumulated as a knowledge base.  ( 2 min )

Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms such as transfer learning, meta learning, and multi-task learning reflect the human learning process by exploiting the prior knowledge for new tasks, encouraging faster learning and good generalization for new tasks. This article gives a detailed view of these learning paradigms and their comparative analysis. The weakness of one learning algorithm turns out to be a strength of another, and thus merging them is a prevalent trait in the literature. There are numerous research papers that focus on each of these learning paradigms separately and provide a comprehensive overview of them. However, this article provides a review of research studies that combine (two of) these learning algorithms. This survey describes how these techniques are combined to solve problems in many different fields of study, including computer vision, natural language processing, hyperspectral imaging, and many more, in supervised setting only. As a result, the global generic learning network an amalgamation of meta learning, transfer learning, and multi-task learning is introduced here, along with some open research questions and future research directions in the multi-task setting.  ( 3 min )

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better ${\bf visual}$ dog classifier by ${\bf read}$ing about dogs and ${\bf listen}$ing to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification.  ( 3 min )

Energy theft detection (ETD) and energy consumption forecasting (ECF) are two interconnected challenges in smart grid systems. Addressing these issues collectively is crucial for ensuring system security. This paper addresses the interconnected challenges of ETD and ECF in smart grid systems. The proposed solution combines long short-term memory (LSTM) and a denoising diffusion probabilistic model (DDPM) to generate input reconstruction and forecasting. By leveraging the reconstruction and forecasting errors, the system identifies instances of energy theft, with the methods based on reconstruction error and forecasting error complementing each other in detecting different types of attacks. Through extensive experiments on real-world and synthetic datasets, the proposed scheme outperforms baseline methods in ETD and ECF problems. The ensemble method significantly enhances ETD performance, accurately detecting energy theft attacks that baseline methods fail to detect. The research offers a comprehensive and effective solution for addressing ETD and ECF challenges, demonstrating promising results and improved security in smart grid systems.  ( 2 min )

Efficient exploration in complex environments remains a major challenge for reinforcement learning (RL). Compared to previous Thompson sampling-inspired mechanisms that enable temporally extended exploration, i.e., deep exploration, we focus on deep exploration in distributional RL. We develop here a general purpose approach, Bag of Policies (BoP), that can be built on top of any return distribution estimator by maintaining a population of its copies. BoP consists of an ensemble of multiple heads that are updated independently. During training, each episode is controlled by only one of the heads and the collected state-action pairs are used to update all heads off-policy, leading to distinct learning signals for each head which diversify learning and behaviour. To test whether optimistic ensemble method can improve on distributional RL as did on scalar RL, by e.g. Bootstrapped DQN, we implement the BoP approach with a population of distributional actor-critics using Bayesian Distributional Policy Gradients (BDPG). The population thus approximates a posterior distribution of return distributions along with a posterior distribution of policies. Another benefit of building upon BDPG is that it allows to analyze global posterior uncertainty along with local curiosity bonus simultaneously for exploration. As BDPG is already an optimistic method, this pairing helps to investigate if optimism is accumulatable in distributional RL. Overall BoP results in greater robustness and speed during learning as demonstrated by our experimental results on ALE Atari games.  ( 2 min )

Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enabling unsupervised representation learning is extremely crucial for time series data, due to its unique annotation bottleneck caused by its complex characteristics and lack of visual cues compared with other data modalities. In recent years, unsupervised representation learning techniques have advanced rapidly in various domains. However, there is a lack of systematic analysis of unsupervised representation learning approaches for time series. To fill the gap, we conduct a comprehensive literature review of existing rapidly evolving unsupervised representation learning approaches for time series. Moreover, we also develop a unified and standardized library, named ULTS (i.e., Unsupervised Learning for Time Series), to facilitate fast implementations and unified evaluations on various models. With ULTS, we empirically evaluate state-of-the-art approaches, especially the rapidly evolving contrastive learning methods, on 9 diverse real-world datasets. We further discuss practical considerations as well as open research challenges on unsupervised representation learning for time series to facilitate future research in this field.

Precise and fast prediction methods for ischemic areas comprised of dead tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS) patients are of significant clinical interest. They play an essential role in improving diagnosis and treatment planning. Computed Tomography (CT) scan is one of the primary modalities for early assessment in patients with suspected AIS. CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions. Current automatic segmentation methods for CTP mostly use already processed 3D parametric maps conventionally used for clinical interpretation by radiologists as input. Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time input, where the spatial information over the volume is ignored. In addition, these methods are only interested in segmenting core regions, while predicting penumbra can be essential for treatment planning. This paper investigates different methods to utilize the entire 4D CTP as input to fully exploit the spatio-temporal information, leading us to propose a novel 4D convolution layer. Our comprehensive experiments on a local dataset of 152 patients divided into three groups show that our proposed models generate more precise results than other methods explored. Adopting the proposed 4D mJ-Net, a Dice Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core areas, respectively. The code is available on https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git.

This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building block for uncertainty estimation methods in a regression setting, for instance Concrete dropout (Gal et al., 2017) and Deep Ensembles (Lakshminarayanan et al., 2017). Specifically, an MVE network assumes that the data is produced from a normal distribution with a mean function and variance function. The MVE network outputs a mean and variance estimate and optimizes the network parameters by minimizing the negative loglikelihood. In our paper, we present two significant insights. Firstly, the convergence difficulties reported in recent work can be relatively easily prevented by following the simple yet often overlooked recommendation from the original authors that a warm-up period should be used. During this period, only the mean is optimized with a fixed variance. We demonstrate the effectiveness of this step through experimentation, highlighting that it should be standard practice. As a sidenote, we examine whether, after the warm-up, it is beneficial to fix the mean while optimizing the variance or to optimize both simultaneously. Here, we do not observe a substantial difference. Secondly, we introduce a novel improvement of the MVE network: separate regularization of the mean and the variance estimate. We demonstrate, both on toy examples and on a number of benchmark UCI regression data sets, that following the original recommendations and the novel separate regularization can lead to significant improvements.

Text-to-image diffusion models such as Stable Diffusion have recently attracted the interest of many researchers, and inverting the diffusion process can play an important role in better understanding the generative process and how to engineer prompts in order to obtain the desired images. To this end, we introduce the new task of predicting the text prompt given an image generated by a generative diffusion model. We combine a series of white-box and black-box models (with and without access to the weights of the diffusion network) to deal with the proposed task. We propose a novel learning framework comprising of a joint prompt regression and multi-label vocabulary classification objective that generates improved prompts. To further improve our method, we employ a curriculum learning procedure that promotes the learning of image-prompt pairs with lower labeling noise (i.e. that are better aligned), and an unsupervised domain-adaptive kernel learning method that uses the similarities between samples in the source and target domains as extra features. We conduct experiments on the DiffusionDB data set, predicting text prompts from images generated by Stable Diffusion. Our novel learning framework produces excellent results on the aforementioned task, yielding the highest gains when applied on the white-box model. In addition, we make an interesting discovery: training a diffusion model on the prompt generation task can make the model generate images that are much better aligned with the input prompts, when the model is directly reused for text-to-image generation.

Stochastic gradient descent (SGD) is the simplest deep learning optimizer with which to train deep neural networks. While SGD can use various learning rates, such as constant or diminishing rates, the previous numerical results showed that SGD performs better than other deep learning optimizers using when it uses learning rates given by line search methods. In this paper, we perform a convergence analysis on SGD with a learning rate given by an Armijo line search for nonconvex optimization. The analysis indicates that the upper bound of the expectation of the squared norm of the full gradient becomes small when the number of steps and the batch size are large. Next, we show that, for SGD with the Armijo-line-search learning rate, the number of steps needed for nonconvex optimization is a monotone decreasing convex function of the batch size; that is, the number of steps needed for nonconvex optimization decreases as the batch size increases. Furthermore, we show that the stochastic first-order oracle (SFO) complexity, which is the stochastic gradient computation cost, is a convex function of the batch size; that is, there exists a critical batch size that minimizes the SFO complexity. Finally, we provide numerical results that support our theoretical results. The numerical results indicate that the number of steps needed for training deep neural networks decreases as the batch size increases and that there exist the critical batch sizes that can be estimated from the theoretical results.

Algorithms for solving the linear classification problem have a long history, dating back at least to 1936 with linear discriminant analysis. For linearly separable data, many algorithms can obtain the exact solution to the corresponding 0-1 loss classification problem efficiently, but for data which is not linearly separable, it has been shown that this problem, in full generality, is NP-hard. Alternative approaches all involve approximations of some kind, including the use of surrogates for the 0-1 loss (for example, the hinge or logistic loss) or approximate combinatorial search, none of which can be guaranteed to solve the problem exactly. Finding efficient algorithms to obtain an exact i.e. globally optimal solution for the 0-1 loss linear classification problem with fixed dimension, remains an open problem. In research we report here, we detail the rigorous construction of a new algorithm, incremental cell enumeration (ICE), that can solve the 0-1 loss classification problem exactly in polynomial time. We prove correctness using concepts from the theory of hyperplane arrangements and oriented matroids. We demonstrate the effectiveness of this algorithm on synthetic and real-world datasets, showing optimal accuracy both in and out-of-sample, in practical computational time. We also empirically demonstrate how the use of approximate upper bound leads to polynomial time run-time improvements to the algorithm whilst retaining exactness. To our knowledge, this is the first, rigorously-proven polynomial time, practical algorithm for this long-standing problem.

We introduce a novel interpretable tree based algorithm for prediction in a regression setting. Our motivation is to estimate the unknown regression function from a functional decomposition perspective in which the functional components correspond to lower order interaction terms. The idea is to modify the random forest algorithm by keeping certain leaves after they are split instead of deleting them. This leads to non-binary trees which we refer to as planted trees. An extension to a forest leads to our random planted forest algorithm. Additionally, the maximum number of covariates which can interact within a leaf can be bounded. If we set this interaction bound to one, the resulting estimator is a sum of one-dimensional functions. In the other extreme case, if we do not set a limit, the resulting estimator and corresponding model place no restrictions on the form of the regression function. In a simulation study we find encouraging prediction and visualisation properties of our random planted forest method. We also develop theory for an idealized version of random planted forests in cases where the interaction bound is low. We show that if it is smaller than three, the idealized version achieves asymptotically optimal convergence rates up to a logarithmic factor. Code is available on GitHub https://github.com/PlantedML/randomPlantedForest.

DNA methylation is a crucial regulator of gene transcription and has been linked to various diseases, including autoimmune diseases and cancers. However, diagnostics based on DNA methylation face challenges due to large feature sets and small sample sizes, resulting in overfitting and suboptimal performance. To address these issues, we propose MIRACLE, a novel interpretable neural network that leverages autoencoder-based multi-task learning to integrate multiple datasets and jointly identify common patterns in DNA methylation. MIRACLE's architecture reflects the relationships between methylation sites, genes, and pathways, ensuring biological interpretability and meaningfulness. The network comprises an encoder and a decoder, with a bottleneck layer representing pathway information as the basic unit of heredity. Customized defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency matrix information, which provides explainability and expresses the site-gene-pathway hierarchical structure explicitly. And from the embedding, there are different multi-task classifiers to predict diseases. Tested on six datasets, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and type 1 diabetes, MIRACLE demonstrates robust performance in identifying common functions of DNA methylation across different phenotypes, with higher accuracy in prediction dieseases than baseline methods. By incorporating biological prior knowledge, MIRACLE offers a meaningful and interpretable framework for DNA methylation data analysis in the context of autoimmune diseases.

In this paper we adapt the nearest neighbour rule to the contextual bandit problem. Our algorithm handles the fully adversarial setting in which no assumptions at all are made about the data-generation process. When combined with a sufficiently fast data-structure for (perhaps approximate) adaptive nearest neighbour search, such as a navigating net, our algorithm is extremely efficient - having a per trial running time polylogarithmic in both the number of trials and actions, and taking only quasi-linear space.

Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links, e.g., social relationships and transaction history, are sensitive and valuable information, which raises privacy concerns when using GNNs. To exploit these vulnerabilities, we propose VertexSerum, a novel graph poisoning attack that increases the effectiveness of graph link stealing by amplifying the link connectivity leakage. To infer node adjacency more accurately, we propose an attention mechanism that can be embedded into the link detection network. Our experiments demonstrate that VertexSerum significantly outperforms the SOTA link inference attack, improving the AUC scores by an average of $9.8\%$ across four real-world datasets and three different GNN structures. Furthermore, our experiments reveal the effectiveness of VertexSerum in both black-box and online learning settings, further validating its applicability in real-world scenarios.

Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF. Our results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes.

How to enable learnability for new classes while keeping the capability well on old classes has been a crucial challenge for class incremental learning. Beyond the normal case, long-tail class incremental learning and few-shot class incremental learning are also proposed to consider the data imbalance and data scarcity, respectively, which are common in real-world implementations and further exacerbate the well-known problem of catastrophic forgetting. Existing methods are specifically proposed for one of the three tasks. In this paper, we offer a unified solution to the misalignment dilemma in the three tasks. Concretely, we propose neural collapse terminus that is a fixed structure with the maximal equiangular inter-class separation for the whole label space. It serves as a consistent target throughout the incremental training to avoid dividing the feature space incrementally. For CIL and LTCIL, we further propose a prototype evolving scheme to drive the backbone features into our neural collapse terminus smoothly. Our method also works for FSCIL with only minor adaptations. Theoretical analysis indicates that our method holds the neural collapse optimality in an incremental fashion regardless of data imbalance or data scarcity. We also design a generalized case where we do not know the total number of classes and whether the data distribution is normal, long-tail, or few-shot for each coming session, to test the generalizability of our method. Extensive experiments with multiple datasets are conducted to demonstrate the effectiveness of our unified solution to all the three tasks and the generalized case.

Multitask learning is a powerful framework that enables one to simultaneously learn multiple related tasks by sharing information between them. Quantifying uncertainty in the estimated tasks is of pivotal importance for many downstream applications, such as online or active learning. In this work, we provide novel multitask confidence intervals in the challenging agnostic setting, i.e., when neither the similarity between tasks nor the tasks' features are available to the learner. The obtained intervals do not require i.i.d. data and can be directly applied to bound the regret in online learning. Through a refined analysis of the multitask information gain, we obtain new regret guarantees that, depending on a task similarity parameter, can significantly improve over treating tasks independently. We further propose a novel online learning algorithm that achieves such improved regret without knowing this parameter in advance, i.e., automatically adapting to task similarity. As a second key application of our results, we introduce a novel multitask active learning setup where several tasks must be simultaneously optimized, but only one of them can be queried for feedback by the learner at each round. For this problem, we design a no-regret algorithm that uses our confidence intervals to decide which task should be queried. Finally, we empirically validate our bounds and algorithms on synthetic and real-world (drug discovery) data.

With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features of CTR prediction are its multi-field categorical data format, and vast and daily-growing data volume. The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances. The self-supervised learning paradigm provides a more promising pretrain-finetune solution to better exploit the large amount of user click logs, and learn more generalized and effective representations. However, self-supervised learning for CTR prediction is still an open question, since current works on this line are only preliminary and rudimentary. To this end, we propose a Model-agnostic pretraining (MAP) framework that applies feature corruption and recovery on multi-field categorical data, and more specifically, we derive two practical algorithms: masked feature prediction (MFP) and replaced feature detection (RFD). MFP digs into feature interactions within each instance through masking and predicting a small portion of input features, and introduces noise contrastive estimation (NCE) to handle large feature spaces. RFD further turns MFP into a binary classification mode through replacing and detecting changes in input features, making it even simpler and more effective for CTR pretraining. Our extensive experiments on two real-world large-scale datasets (i.e., Avazu, Criteo) demonstrate the advantages of these two methods on several strong backbones (e.g., DCNv2, DeepFM), and achieve new state-of-the-art performance in terms of both effectiveness and efficiency for CTR prediction.

Diffusion models have emerged as the \emph{de-facto} technique for image generation, yet they entail significant computational overhead, hindering the technique's broader application in the research community. We propose a prior-based denoising training framework, the first to incorporate the pre-train and fine-tune paradigm into the diffusion model training process, which substantially improves training efficiency and shows potential in facilitating various downstream tasks. Our approach centers on masking a high proportion (e.g., up to 90\%) of the input image and employing masked denoising score matching to denoise the visible areas, thereby guiding the diffusion model to learn more salient features from training data as prior knowledge. By utilizing masked learning in a pre-training stage, we efficiently train the ViT-based diffusion model on CelebA-HQ $256 \times 256$ in the pixel space, achieving a 4x acceleration and enhancing the quality of generated images compared to denoising diffusion probabilistic model (DDPM). Moreover, our masked pre-training technique can be universally applied to various diffusion models that directly generate images in the pixel space, aiding in the learning of pre-trained models with superior generalizability. For instance, a diffusion model pre-trained on VGGFace2 attains a 46\% quality improvement through fine-tuning with merely 10\% data from a different distribution. Moreover, our method shows the potential to serve as a training paradigm for enhancing the privacy protection capabilities of diffusion models. Our code is available at \url{https://github.com/jiachenlei/maskdm}.

We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. Our empirical findings provide compelling evidence that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. However, modeling nonlinear spillover effects enhances the forecasting accuracy of realized volatilities, particularly for short-term horizons of up to one week. Moreover, our results consistently indicate that training with the Quasi-likelihood loss leads to substantial improvements in model performance compared to the commonly-used mean squared error. A comprehensive series of empirical evaluations in alternative settings confirm the robustness of our results.

We consider the inverse acoustic obstacle problem for sound-soft star-shaped obstacles in two dimensions wherein the boundary of the obstacle is determined from measurements of the scattered field at a collection of receivers outside the object. One of the standard approaches for solving this problem is to reformulate it as an optimization problem: finding the boundary of the domain that minimizes the $L^2$ distance between computed values of the scattered field and the given measurement data. The optimization problem is computationally challenging since the local set of convexity shrinks with increasing frequency and results in an increasing number of local minima in the vicinity of the true solution. In many practical experimental settings, low frequency measurements are unavailable due to limitations of the experimental setup or the sensors used for measurement. Thus, obtaining a good initial guess for the optimization problem plays a vital role in this environment. We present a neural network warm-start approach for solving the inverse scattering problem, where an initial guess for the optimization problem is obtained using a trained neural network. We demonstrate the effectiveness of our method with several numerical examples. For high frequency problems, this approach outperforms traditional iterative methods such as Gauss-Newton initialized without any prior (i.e., initialized using a unit circle), or initialized using the solution of a direct method such as the linear sampling method. The algorithm remains robust to noise in the scattered field measurements and also converges to the true solution for limited aperture data. However, the number of training samples required to train the neural network scales exponentially in frequency and the complexity of the obstacles considered. We conclude with a discussion of this phenomenon and potential directions for future research.

We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the Combined Actuarial Neural Network (CANN) framework proposed by Mario W\"uthrich and Michael Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part. The MLP part is used to process telematics car driving data given as a vector characterizing the driving behavior of each insured driver. In addition to the Poisson and negative binomial distributions for cross-sectional data, we propose a procedure for training our CANN model with a multivariate negative binomial (MVNB) specification. By doing so, we introduce a longitudinal model that accounts for the dependence between contracts from the same insured. Our results reveal that the CANN models exhibit superior performance compared to log-linear models that rely on manually engineered telematics features.

A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose, investigating how existing malware families are developed and examining emerging families need to be explored. This paper focuses on the online processing of incoming malicious samples to assign them to existing families or, in the case of samples from new families, to cluster them. We experimented with seven prevalent malware families from the EMBER dataset, four in the training set and three additional new families in the test set. Based on the classification score of the multilayer perceptron, we determined which samples would be classified and which would be clustered into new malware families. We classified 97.21% of streaming data with a balanced accuracy of 95.33%. Then, we clustered the remaining data using a self-organizing map, achieving a purity from 47.61% for four clusters to 77.68% for ten clusters. These results indicate that our approach has the potential to be applied to the classification and clustering of zero-day malware into malware families.

The turbulent jet ignition concept using prechambers is a promising solution to achieve stable combustion at lean conditions in large gas engines, leading to high efficiency at low emission levels. Due to the wide range of design and operating parameters for large gas engine prechambers, the preferred method for evaluating different designs is computational fluid dynamics (CFD), as testing in test bed measurement campaigns is time-consuming and expensive. However, the significant computational time required for detailed CFD simulations due to the complexity of solving the underlying physics also limits its applicability. In optimization settings similar to the present case, i.e., where the evaluation of the objective function(s) is computationally costly, Bayesian optimization has largely replaced classical design-of-experiment. Thus, the present study deals with the computationally efficient Bayesian optimization of large gas engine prechambers design using CFD simulation. Reynolds-averaged-Navier-Stokes simulations are used to determine the target values as a function of the selected prechamber design parameters. The results indicate that the chosen strategy is effective to find a prechamber design that achieves the desired target values.

A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory forecasting of the detected objects, or predict dense occupancy and flow grids for the whole scene. The former poses a safety concern as the number of detections needs to be kept low for efficiency reasons, sacrificing object recall. The latter is computationally expensive due to the high-dimensionality of the output grid, and suffers from the limited receptive field inherent to fully convolutional networks. Furthermore, both approaches employ many computational resources predicting areas or objects that might never be queried by the motion planner. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network. Our method avoids unnecessary computation, as it can be directly queried by the motion planner at continuous spatio-temporal locations. Moreover, we design an architecture that overcomes the limited receptive field of previous explicit occupancy prediction methods by adding an efficient yet effective global attention mechanism. Through extensive experiments in both urban and highway settings, we demonstrate that our implicit model outperforms the current state-of-the-art. For more information, visit the project website: https://waabi.ai/research/implicito.

With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. One of the most prominent uncertainty estimation methods is Deep Ensembles (Lakshminarayanan et al., 2017) . A classical parametric model has uncertainty in the parameters due to the fact that the data on which the model is build is a random sample. A modern neural network has an additional uncertainty component since the optimization of the network is random. Lakshminarayanan et al. (2017) noted that Deep Ensembles do not incorporate the classical uncertainty induced by the effect of finite data. In this paper, we present a computationally cheap extension of Deep Ensembles for the regression setting, called Bootstrapped Deep Ensembles, that explicitly takes this classical effect of finite data into account using a modified version of the parametric bootstrap. We demonstrate through an experimental study that our method significantly improves upon standard Deep Ensembles

While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law which possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.

Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cross-modal representation learning from different data types -- such as images and time-series data (e.g., audio or text data) -- requires a deep metric learning loss that minimizes the distance between the modality embeddings. In this paper, we propose to use the contrastive or triplet loss, which uses positive and negative identities to create sample pairs with different labels, for cross-modal representation learning between image and time-series modalities (CMR-IS). By adapting the triplet loss for cross-modal representation learning, higher accuracy in the main (time-series classification) task can be achieved by exploiting additional information of the auxiliary (image classification) task. We present a triplet loss with a dynamic margin for single label and sequence-to-sequence classification tasks. We perform extensive evaluations on synthetic image and time-series data, and on data for offline handwriting recognition (HWR) and on online HWR from sensor-enhanced pens for classifying written words. Our experiments show an improved classification accuracy, faster convergence, and better generalizability due to an improved cross-modal representation. Furthermore, the more suitable generalizability leads to a better adaptability between writers for online HWR.

We present a latent variable generalisation of neural network softmax classification trained with cross-entropy loss, referred to as variational classification (VC). Our approach offers a novel probabilistic perspective on the highly familiar softmax classification model, to which it relates similarly to how variational and traditional autoencoders relate. We derive a training objective based on the evidence lower bound (ELBO) that is non-trivial to optimize, and therefore propose an adversarial approach to maximise it. We show that VC addresses an inherent inconsistency within softmax classification, whilst also allowing more flexible choices of prior distributions in the latent space in place of implicit assumptions revealed within off-the-shelf softmax classifiers. Empirical evaluation on image and text classification datasets demonstrates that variational classification maintains prediction accuracy while improving other desirable properties such as calibration and adversarial robustness, particularly under distribution shift and low data settings.

Maintaining a balance between the supply and demand of products by optimizing replenishment decisions is one of the most important challenges in the supply chain industry. This paper presents a novel reinforcement learning framework called MARLIM, to address the inventory management problem for a single-echelon multi-products supply chain with stochastic demands and lead-times. Within this context, controllers are developed through single or multiple agents in a cooperative setting. Numerical experiments on real data demonstrate the benefits of reinforcement learning methods over traditional baselines.

In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit structure by adding and removing gates, and optimize their parameters during the training. Several Adaptative VQAs, based on heuristics such as circuit shallowness, entanglement capability and hardware compatibility, have already been proposed in the literature, but there is still lack of a systematic comparison between the different methods. In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline. In order to compare these algorithms to traditional VQAs, we also include the Quantum Approximate Optimization Algorithm (QAOA) in our analysis. We apply these algorithms to QUBO problems and study their performance by examining the quality of the solutions found and the computational times required. Additionally, we investigate how the choice of the hyperparameters can impact the overall performance of the algorithms, highlighting the importance of selecting an appropriate methodology for hyperparameter tuning. Our analysis sets benchmarks for Adaptative VQAs designed for near-term quantum devices and provides valuable insights to guide future research in this area.

For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of correctness for subsequent decision-making. Existing dense binary classification models are prone to being over-confident. To improve model calibration, we propose Adaptive Stochastic Label Perturbation (ASLP) which learns a unique label perturbation level for each training image. ASLP employs our proposed Self-Calibrating Binary Cross Entropy (SC-BCE) loss, which unifies label perturbation processes including stochastic approaches (like DisturbLabel), and label smoothing, to correct calibration while maintaining classification rates. ASLP follows Maximum Entropy Inference of classic statistical mechanics to maximise prediction entropy with respect to missing information. It performs this while: (1) preserving classification accuracy on known data as a conservative solution, or (2) specifically improves model calibration degree by minimising the gap between the prediction accuracy and expected confidence of the target training label. Extensive results demonstrate that ASLP can significantly improve calibration degrees of dense binary classification models on both in-distribution and out-of-distribution data. The code is available on https://github.com/Carlisle-Liu/ASLP.

Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate. In this paper, we consider the inverse problem -- given a collection of different images, can we discover the generative concepts that represent each image? We present an unsupervised approach to discover generative concepts from a collection of images, disentangling different art styles in paintings, objects, and lighting from kitchen scenes, and discovering image classes given ImageNet images. We show how such generative concepts can accurately represent the content of images, be recombined and composed to generate new artistic and hybrid images, and be further used as a representation for downstream classification tasks.

Diffusion models and large language models have emerged as leading-edge generative models and have sparked a revolutionary impact on various aspects of human life. However, the practical implementation of these models has also exposed inherent risks, highlighting their dual nature and raising concerns regarding their trustworthiness. Despite the abundance of literature on this subject, a comprehensive survey specifically delving into the intersection of large-scale generative models and their trustworthiness remains largely absent. To bridge this gap, This paper investigates both the long-standing and emerging threats associated with these models across four fundamental dimensions: privacy, security, fairness, and responsibility. In this way, we construct an extensive map outlining the trustworthiness of these models, while also providing practical recommendations and identifying future directions. These efforts are crucial for promoting the trustworthy deployment of these models, ultimately benefiting society as a whole.

We deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objectives are considered. Under this setting, each node only controls a part of the global decision, and the goal of all nodes is to collaboratively minimize the global cost over a time horizon $T$ while guarantees the security of the transmitted information. For such problems, we first design a novel generic algorithm framework, named as DPSDA, of differentially private distributed online learning using the Laplace mechanism and the stochastic variants of dual averaging method. Note that in the dual updates, all nodes of DPSDA employ the noise-corrupted gradients for more generality. Then, we propose two algorithms, named as DPSDA-C and DPSDA-PS, under this framework. In DPSDA-C, the nodes implement a circulation-based communication in the primal updates so as to alleviate the disagreements over time-varying undirected networks. In addition, for the extension to time-varying directed ones, the nodes implement the broadcast-based push-sum dynamics in DPSDA-PS, which can achieve average consensus over arbitrary directed networks. Theoretical results show that both algorithms attain an expected regret upper bound in $\mathcal{O}( \sqrt{T} )$ when the objective function is convex, which matches the best utility achievable by cutting-edge algorithms. Finally, numerical experiment results on both synthetic and real-world datasets verify the effectiveness of our algorithms.

Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simulation (LES) has been widely used as an alternative to direct numerical simulation (DNS) for simulating turbulent flows due to its reduced computational cost. However, LES is unable to capture all of the scales of turbulent transport accurately. Reconstructing DNS from low-resolution LES is critical for many scientific and engineering disciplines, but it poses many challenges to existing super-resolution methods due to the spatio-temporal complexity of turbulent flows. In this work, we propose a new physics-guided neural network for reconstructing the sequential DNS from low-resolution LES data. The proposed method leverages the partial differential equation that underlies the flow dynamics in the design of spatio-temporal model architecture. A degradation-based refinement method is also developed to enforce physical constraints and further reduce the accumulated reconstruction errors over long periods. The results on two different types of turbulent flow data confirm the superiority of the proposed method in reconstructing the high-resolution DNS data and preserving the physical characteristics of flow transport.

The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. Most recent contributions in the field of morphological classification of extragalactic radio sources have proposed classifiers based on convolutional neural networks. Alternatively, this work proposes gradient boosting machine learning methods accompanied by principal component analysis as data-efficient alternatives to convolutional neural networks. Recent findings have shown the efficacy of gradient boosting methods in outperforming deep learning methods for classification problems with tabular data. The gradient boosting methods considered in this work are based on the XGBoost, LightGBM, and CatBoost implementations. This work also studies the effect of dataset size on classifier performance. A three-class classification problem is considered in this work based on the three main Fanaroff-Riley classes: class 0, class I, and class II, using radio sources from the Best-Heckman sample. All three proposed gradient boosting methods outperformed a state-of-the-art convolutional neural networks-based classifier using less than a quarter of the number of images, with CatBoost having the highest accuracy. This was mainly due to the superior accuracy of gradient boosting methods in classifying Fanaroff-Riley class II sources, with 3$\unicode{x2013}$4% higher recall.

Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decision-making process.

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the time series causal discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.

While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.

Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. This is where the single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. Nonetheless, we discover a critical issue of single-path DARTS that has not been primarily noticed. Namely, it also suffers from severe performance collapse since too many parameter-free operations like skip connections are derived, just like DARTS does. In this paper, we propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure. First, we disentangle the topology search from the operation search to make searching and evaluation consistent. We then adopt Gumbel-Top2 reparameterization and gradient accumulation to robustify the unwieldy bi-level optimization. We verify ROME extensively across 15 benchmarks to demonstrate its effectiveness and robustness.

We consider the degree-Rips construction from topological data analysis, which provides a density-sensitive, multiparameter hierarchical clustering algorithm. We analyze its stability to perturbations of the input data using the correspondence-interleaving distance, a metric for hierarchical clusterings that we introduce. Taking certain one-parameter slices of degree-Rips recovers well-known methods for density-based clustering, but we show that these methods are unstable. However, we prove that degree-Rips, as a multiparameter object, is stable, and we propose an alternative approach for taking slices of degree-Rips, which yields a one-parameter hierarchical clustering algorithm with better stability properties. We prove that this algorithm is consistent, using the correspondence-interleaving distance. We provide an algorithm for extracting a single clustering from one-parameter hierarchical clusterings, which is stable with respect to the correspondence-interleaving distance. And, we integrate these methods into a pipeline for density-based clustering, which we call Persistable. Adapting tools from multiparameter persistent homology, we propose visualization tools that guide the selection of all parameters of the pipeline. We demonstrate Persistable on benchmark datasets, showing that it identifies multi-scale cluster structure in data.

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.

Recently, significant advancements have been made in time-series forecasting research, with an increasing focus on analyzing the nature of time-series data, e.g, channel-independence (CI) and channel-dependence (CD), rather than solely focusing on designing sophisticated forecasting models. However, current research has primarily focused on either CI or CD in isolation, and the challenge of effectively combining these two opposing properties to achieve a synergistic effect remains an unresolved issue. In this paper, we carefully examine the opposing properties of CI and CD, and raise a practical question that has not been effectively answered, e.g.,"How to effectively mix the CI and CD properties of time series to achieve better predictive performance?" To answer this question, we propose Mlinear (MIX-Linear), a simple yet effective method based mainly on linear layers. The design philosophy of Mlinear mainly includes two aspects:(1) dynamically tuning the CI and CD properties based on the time semantics of different input time series, and (2) providing deep supervision to adjust the individual performance of the "CI predictor" and "CD predictor". In addition, empirically, we introduce a new loss function that significantly outperforms the widely used mean squared error (MSE) on multiple datasets. Experiments on time-series datasets covering multiple fields and widely used have demonstrated the superiority of our method over PatchTST which is the lateset Transformer-based method in terms of the MSE and MAE metrics on 7 datasets with identical sequence inputs (336 or 512). Specifically, our method significantly outperforms PatchTST with a ratio of 21:3 at 336 sequence length input and 29:10 at 512 sequence length input. Additionally, our approach has a 10 $\times$ efficiency advantage at the unit level, taking into account both training and inference times.

While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth. Owing to these practical constraints and system models, this paper leverages model pruning and proposes a pruning-enabled hierarchical federated learning (PHFL) in heterogeneous networks (HetNets). We first derive an upper bound of the convergence rate that clearly demonstrates the impact of the model pruning and wireless communications between the clients and the associated BS. Then we jointly optimize the model pruning ratio, central processing unit (CPU) frequency and transmission power of the clients in order to minimize the controllable terms of the convergence bound under strict delay and energy constraints. However, since the original problem is not convex, we perform successive convex approximation (SCA) and jointly optimize the parameters for the relaxed convex problem. Through extensive simulation, we validate the effectiveness of our proposed PHFL algorithm in terms of test accuracy, wall clock time, energy consumption and bandwidth requirement.

The COVID-19 pandemic presented numerous challenges to healthcare systems worldwide. Given that lung infections are prevalent among COVID-19 patients, chest Computer Tomography (CT) scans have frequently been utilized as an alternative method for identifying COVID-19 conditions and various other types of pulmonary diseases. Deep learning architectures have emerged to automate the identification of pulmonary disease types by leveraging CT scan slices as inputs for classification models. This paper introduces COVID-VR, a novel approach for classifying pulmonary diseases based on volume rendering images of the lungs captured from multiple angles, thereby providing a comprehensive view of the entire lung in each image. To assess the effectiveness of our proposal, we compared it against competing strategies utilizing both private data obtained from partner hospitals and a publicly available dataset. The results demonstrate that our approach effectively identifies pulmonary lesions and performs competitively when compared to slice-based methods.

Large language models (LLMs) have revolutionized NLP by solving downstream tasks with little to no labeled data. Despite their versatile abilities, the larger question of their ability to reason remains ill-understood. This paper addresses reasoning in math word problems (MWPs) by studying symbolic versions of the numeric problems, since a symbolic expression is a "concise explanation" of the numeric answer. We create and use a symbolic version of the SVAMP dataset and find that GPT-3's davinci-002 model also has good zero-shot accuracy on symbolic MWPs. To evaluate the faithfulness of the model's reasoning, we go beyond accuracy and additionally evaluate the alignment between the final answer and the outputted reasoning, which correspond to numeric and symbolic answers respectively for MWPs. We explore a self-prompting approach to encourage the symbolic reasoning to align with the numeric answer, thus equipping the LLM with the ability to provide a concise and verifiable reasoning and making it more interpretable. Surprisingly, self-prompting also improves the symbolic accuracy to be higher than both the numeric and symbolic accuracies, thus providing an ensembling effect. The SVAMP_Sym dataset will be released for future research on symbolic math problems.

Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE). Nevertheless, recent studies have revealed that adversarially trained models are prone to unfairness problems, restricting their applicability. In this paper, we empirically observe that this limitation may be attributed to serious adversarial confidence overfitting, i.e., certain adversarial examples with overconfidence. To alleviate this problem, we propose HAM, a straightforward yet effective framework via adaptive Hard Adversarial example Mining.HAM concentrates on mining hard adversarial examples while discarding the easy ones in an adaptive fashion. Specifically, HAM identifies hard AEs in terms of their step sizes needed to cross the decision boundary when calculating loss value. Besides, an early-dropping mechanism is incorporated to discard the easy examples at the initial stages of AE generation, resulting in efficient AT. Extensive experimental results on CIFAR-10, SVHN, and Imagenette demonstrate that HAM achieves significant improvement in robust fairness while reducing computational cost compared to several state-of-the-art adversarial training methods. The code will be made publicly available.

ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance. However, the current landscape lacks an accessible, efficient, and cost-effective end-to-end RLHF (Reinforcement Learning with Human Feedback) training pipeline for these powerful models, particularly when training at the scale of billions of parameters. This paper introduces DeepSpeed-Chat, a novel system that democratizes RLHF training, making it accessible to the AI community. DeepSpeed-Chat offers three key capabilities: an easy-to-use training and inference experience for ChatGPT-like models, a DeepSpeed-RLHF pipeline that replicates the training pipeline from InstructGPT, and a robust DeepSpeed-RLHF system that combines various optimizations for training and inference in a unified way. The system delivers unparalleled efficiency and scalability, enabling training of models with hundreds of billions of parameters in record time and at a fraction of the cost. With this development, DeepSpeed-Chat paves the way for broader access to advanced RLHF training, even for data scientists with limited resources, thereby fostering innovation and further development in the field of AI.

This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial wall. We first proposed a U-Net based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches, respectively, to further improve the prediction accuracy of field maps. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model's performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.

In peer review systems, reviewers are often asked to evaluate various features of submissions, such as technical quality or novelty. A score is given to each of the predefined features and based on these the reviewer has to provide an overall quantitative recommendation. It may be assumed that each reviewer has her own mapping from the set of features to a recommendation, and that different reviewers have different mappings in mind. This introduces an element of arbitrariness known as commensuration bias. In this paper we discuss a framework, introduced by Noothigattu, Shah and Procaccia, and then applied by the organizers of the AAAI 2022 conference. Noothigattu, Shah and Procaccia proposed to aggregate reviewer's mapping by minimizing certain loss functions, and studied axiomatic properties of this approach, in the sense of social choice theory. We challenge several of the results and assumptions used in their work and report a number of negative results. On the one hand, we study a trade-off between some of the axioms proposed and the ability of the method to properly capture agreements of the majority of reviewers. On the other hand, we show that dropping a certain unrealistic assumption has dramatic effects, including causing the method to be discontinuous.

At the beginning era of large language model, it is quite critical to generate a high-quality financial dataset to fine-tune a large language model for financial related tasks. Thus, this paper presents a carefully designed data creation pipeline for this purpose. Particularly, we initiate a dialogue between an AI investor and financial expert using ChatGPT and incorporate the feedback of human financial experts, leading to the refinement of the dataset. This pipeline yielded a robust instruction tuning dataset comprised of 103k multi-turn chats. Extensive experiments have been conducted on this dataset to evaluate the model's performance by adopting an external GPT-4 as the judge. The promising experimental results verify that our approach led to significant advancements in generating accurate, relevant, and financial-style responses from AI models, and thus providing a powerful tool for applications within the financial sector.

The presence of label noise in the training data has a profound impact on the generalization of deep neural networks (DNNs). In this study, we introduce and theoretically demonstrate a simple feature noise method, which directly adds noise to the features of training data, can enhance the generalization of DNNs under label noise. Specifically, we conduct theoretical analyses to reveal that label noise leads to weakened DNN generalization by loosening the PAC-Bayes generalization bound, and feature noise results in better DNN generalization by imposing an upper bound on the mutual information between the model weights and the features, which constrains the PAC-Bayes generalization bound. Furthermore, to ensure effective generalization of DNNs in the presence of label noise, we conduct application analyses to identify the optimal types and levels of feature noise to add for obtaining desirable label noise generalization. Finally, extensive experimental results on several popular datasets demonstrate the feature noise method can significantly enhance the label noise generalization of the state-of-the-art label noise method.

Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music.

A common way to explore text corpora is through low-dimensional projections of the documents, where one hopes that thematically similar documents will be clustered together in the projected space. However, popular algorithms for dimensionality reduction of text corpora, like Latent Dirichlet Allocation (LDA), often produce projections that do not capture human notions of document similarity. We propose a semi-supervised human-in-the-loop LDA-based method for learning topics that preserve semantically meaningful relationships between documents in low-dimensional projections. On synthetic corpora, our method yields more interpretable projections than baseline methods with only a fraction of labels provided. On a real corpus, we obtain qualitatively similar results.

Depth completion, which aims to generate high-quality dense depth maps from sparse depth maps, has attracted increasing attention in recent years. Previous work usually employs RGB images as guidance, and introduces iterative spatial propagation to refine estimated coarse depth maps. However, most of the propagation refinement methods require several iterations and suffer from a fixed receptive field, which may contain irrelevant and useless information with very sparse input. In this paper, we address these two challenges simultaneously by revisiting the idea of deformable convolution. We propose an effective architecture that leverages deformable kernel convolution as a single-pass refinement module, and empirically demonstrate its superiority. To better understand the function of deformable convolution and exploit it for depth completion, we further systematically investigate a variety of representative strategies. Our study reveals that, different from prior work, deformable convolution needs to be applied on an estimated depth map with a relatively high density for better performance. We evaluate our model on the large-scale KITTI dataset and achieve state-of-the-art level performance in both accuracy and inference speed. Our code is available at https://github.com/AlexSunNik/ReDC.

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply Semi-Supervised Learning (SSL) techniques for improved performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. To fulfill this, we propose a novel LNL framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high confidence selection technique that selects those examples with high confidence scores and matched predictions with given labels to dynamically expand a base clean sample set. To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples. Extensive experiments demonstrate that ProMix significantly advances the current state-of-the-art results on multiple benchmarks with different types and levels of noise. It achieves an average improvement of 2.48\% on the CIFAR-N dataset. The code is available at https://github.com/Justherozen/ProMix

Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundamental task to foster their adoption, but validating explanations for link prediction models has received little attention. In this paper, we provide quantitative metrics to assess the quality of link prediction explanations, with or without ground-truth. State-of-the-art explainability methods for Graph Neural Networks are evaluated using these metrics. We discuss how underlying assumptions and technical details specific to the link prediction task, such as the choice of distance between node embeddings, can influence the quality of the explanations.

Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On the flip side, these models have numerous drawbacks, including their potential to generate images featuring sexually explicit content, mirror artistic styles without permission, or even hallucinate (or deepfake) the likenesses of celebrities. Consequently, various methods have been proposed in order to "erase" sensitive concepts from text-to-image models. In this work, we examine five recently proposed concept erasure methods, and show that targeted concepts are not fully excised from any of these methods. Specifically, we leverage the existence of special learned word embeddings that can retrieve "erased" concepts from the sanitized models with no alterations to their weights. Our results highlight the brittleness of post hoc concept erasure methods, and call into question their use in the algorithmic toolkit for AI safety.

An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.

This paper presents a fully automated approach for identifying speech anomalies from voice recordings to aid in the assessment of speech impairments. By combining Connectionist Temporal Classification (CTC) and encoder-decoder-based automatic speech recognition models, we generate rich acoustic and clean transcripts. We then apply several natural language processing methods to extract features from these transcripts to produce prototypes of healthy speech. Basic distance measures from these prototypes serve as input features for standard machine learning classifiers, yielding human-level accuracy for the distinction between recordings of people with aphasia and a healthy control group. Furthermore, the most frequently occurring aphasia types can be distinguished with 90% accuracy. The pipeline is directly applicable to other diseases and languages, showing promise for robustly extracting diagnostic speech biomarkers.

Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful applications of Graph Neural Networks (GNNs), Hypergraph Neural Networks (HGNNs), and their well-designed variants, on some commonly used benchmark graphs and hypergraphs, they are outperformed by even a simple Multi-Layer Perceptron. This observation motivates a reexamination of the design paradigm of the current GNNs and HGNNs and poses challenges of extracting graph features effectively. In this work, a universal feature encoder for both graph and hypergraph representation learning is designed, called UniG-Encoder. The architecture starts with a forward transformation of the topological relationships of connected nodes into edge or hyperedge features via a normalized projection matrix. The resulting edge/hyperedge features, together with the original node features, are fed into a neural network. The encoded node embeddings are then derived from the reversed transformation, described by the transpose of the projection matrix, of the network's output, which can be further used for tasks such as node classification. The proposed architecture, in contrast to the traditional spectral-based and/or message passing approaches, simultaneously and comprehensively exploits the node features and graph/hypergraph topologies in an efficient and unified manner, covering both heterophilic and homophilic graphs. The designed projection matrix, encoding the graph features, is intuitive and interpretable. Extensive experiments are conducted and demonstrate the superior performance of the proposed framework on twelve representative hypergraph datasets and six real-world graph datasets, compared to the state-of-the-art methods. Our implementation is available online at https://github.com/MinhZou/UniG-Encoder.

Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications such as home robots and medical diagnoses. One significant challenge is to devise a robust decentralized learning framework for various client models where centralized data collection is refrained due to confidentiality concerns. This work aims to tackle privacy-preserving VQA by decoupling a multi-modal model into representation modules and a contrastive module and leveraging inter-module gradients sharing and inter-client weight sharing. To this end, we propose Bidirectional Contrastive Split Learning (BiCSL) to train a global multi-modal model on the entire data distribution of decentralized clients. We employ the contrastive loss that enables a more efficient self-supervised learning of decentralized modules. Comprehensive experiments are conducted on the VQA-v2 dataset based on five SOTA VQA models, demonstrating the effectiveness of the proposed method. Furthermore, we inspect BiCSL's robustness against a dual-key backdoor attack on VQA. Consequently, BiCSL shows much better robustness to the multi-modal adversarial attack compared to the centralized learning method, which provides a promising approach to decentralized multi-modal learning.

We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels. Deep neural networks are used to upscale artificially downscaled patches of levels from the puzzle platformer game Lode Runner. The trained networks are incorporated into a web-based editor, where the user can create and edit levels at three different levels of resolution: 4x4, 8x8, and 16x16. An edit at any resolution instantly transfers to the other resolutions. As upscaling requires inventing features that might not be present at lower resolutions, we train neural networks to reproduce these features. We introduce a neural network architecture that is capable of not only learning upscaling but also giving higher priority to less frequent tiles. To investigate the potential of this tool and guide further development, we conduct a qualitative study with 3 designers to understand how they use it. Designers enjoyed co-designing with the tool, liked its underlying concept, and provided feedback for further improvement.

An attractive book cover is important for the success of a book. In this paper, we apply Generative Adversarial Networks (GANs) to the book covers domain, using different methods for training in order to obtain better generated images. We interleave GANs with knowledge graphs to alter the input title to obtain multiple possible options for any given title, which are then used as an augmented input to the generator. Finally, we use the discriminator obtained during the training phase to select the best images generated with new titles. Our method performed better at generating book covers than previous attempts, and the knowledge graph gives better options to the book author or editor compared to using GANs alone.

Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment, which scales poorly as the pool of the robot models expands and the variety of the operating environment increases. In this paper, we propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system. Although being designed to provide great flexibility to the choice of simulators and real hardware, our system can still achieve great performance. For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to better imitation learning performance, compared with a previous system that is particularly designed for that simulator. Project page: this http URL

Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance reported from different embedding learning algorithms, few efforts were devoted to structurally interpreting how features are encoded in the learned embedding space. This work proposes EmbeddingTree, a hierarchical embedding exploration algorithm that relates the semantics of entity features with the less-interpretable embedding vectors. An interactive visualization tool is also developed based on EmbeddingTree to explore high-dimensional embeddings. The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities. We demonstrate the efficacy of EmbeddingTree and our visualization tool through embeddings generated for industry-scale merchant data and the public 30Music listening/playlists dataset.

Distribution shifts are a serious concern in modern statistical learning as they can systematically change the properties of the data away from the truth. We focus on Wasserstein distribution shifts, where every data point may undergo a slight perturbation, as opposed to the Huber contamination model where a fraction of observations are outliers. We formulate and study shifts beyond independent perturbations, exploring Joint Distribution Shifts, where the per-observation perturbations can be coordinated. We analyze several important statistical problems, including location estimation, linear regression, and non-parametric density estimation. Under a squared loss for mean estimation and prediction error in linear regression, we find the exact minimax risk, a least favorable perturbation, and show that the sample mean and least squares estimators are respectively optimal. This holds for both independent and joint shifts, but the least favorable perturbations and minimax risks differ. For other problems, we provide nearly optimal estimators and precise finite-sample bounds. We also introduce several tools for bounding the minimax risk under distribution shift, such as a smoothing technique for location families, and generalizations of classical tools including least favorable sequences of priors, the modulus of continuity, Le Cam's, Fano's, and Assouad's methods.

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive efforts on improving both empirical and provable robustness against evasion attacks; however, the provable robustness against backdoor attacks still remains largely unexplored. In this paper, we focus on certifying the machine learning model robustness against general threat models, especially backdoor attacks. We first provide a unified framework via randomized smoothing techniques and show how it can be instantiated to certify the robustness against both evasion and backdoor attacks. We then propose the first robust training process, RAB, to smooth the trained model and certify its robustness against backdoor attacks. We prove the robustness bound for machine learning models trained with RAB and prove that our robustness bound is tight. In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm that eliminates the need to sample from a noise distribution for such models. Empirically, we conduct comprehensive experiments for different machine learning (ML) models such as DNNs, support vector machines, and K-NN models on MNIST, CIFAR-10, and ImageNette datasets and provide the first benchmark for certified robustness against backdoor attacks. In addition, we evaluate K-NN models on a spambase tabular dataset to demonstrate the advantages of the proposed exact algorithm. Both the theoretic analysis and the comprehensive evaluation on diverse ML models and datasets shed light on further robust learning strategies against general training time attacks.

This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems. With comparable performance on image classification task, it allows for the modification of the weights via a continuous group of symmetry. This is a significant shift from traditional models where the architecture and weights are essentially fixed. We wish to promote the (internal) symmetry as a new desirable property for a neural network, and to draw attention to the PDE perspective in analyzing and interpreting ConvNets in the broader Deep Learning community.

In recent years, multiple notions of algorithmic fairness have arisen. One such notion is individual fairness (IF), which requires that individuals who are similar receive similar treatment. In parallel, matrix estimation (ME) has emerged as a natural paradigm for handling noisy data with missing values. In this work, we connect the two concepts. We show that pre-processing data using ME can improve an algorithm's IF without sacrificing performance. Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions. We then show that, under analogous conditions, SVT pre-processing also yields estimates that are consistent and approximately minimax optimal. As such, the ME pre-processing step does not, under the stated conditions, increase the prediction error of the base algorithm, i.e., does not impose a fairness-performance trade-off. We verify these results on synthetic and real data.

Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.

Going beyond stochastic gradient descent (SGD), what new phenomena emerge in wide neural networks trained by adaptive optimizers like Adam? Here we show: The same dichotomy between feature learning and kernel behaviors (as in SGD) holds for general optimizers as well, including Adam -- albeit with a nonlinear notion of "kernel." We derive the corresponding "neural tangent" and "maximal update" limits for any architecture. Two foundational advances underlie the above results: 1) A new Tensor Program language, NEXORT, that can express how adaptive optimizers process gradients into updates. 2) The introduction of bra-ket notation to drastically simplify expressions and calculations in Tensor Programs. This work summarizes and generalizes all previous results in the Tensor Programs series of papers.

A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper investigates the integration of two frameworks for tackling those goals: episodic control and successor features. Episodic control is a cognitively inspired approach relying on episodic memory, an instance-based memory model of an agent's experiences. Meanwhile, successor features and generalized policy improvement (SF&GPI) is a meta and transfer learning framework allowing to learn policies for tasks that can be efficiently reused for later tasks which have a different reward function. Individually, these two techniques have shown impressive results in vastly improving sample efficiency and the elegant reuse of previously learned policies. Thus, we outline a combination of both approaches in a single reinforcement learning framework and empirically illustrate its benefits.

Knowing the actual precipitation in space and time is critical in hydrological modelling applications, yet the spatial coverage with rain gauge stations is limited due to economic constraints. Gridded satellite precipitation datasets offer an alternative option for estimating the actual precipitation by covering uniformly large areas, albeit related estimates are not accurate. To improve precipitation estimates, machine learning is applied to merge rain gauge-based measurements and gridded satellite precipitation products. In this context, observed precipitation plays the role of the dependent variable, while satellite data play the role of predictor variables. Random forests is the dominant machine learning algorithm in relevant applications. In those spatial predictions settings, point predictions (mostly the mean or the median of the conditional distribution) of the dependent variable are issued. The aim of the manuscript is to solve the problem of probabilistic prediction of precipitation with an emphasis on extreme quantiles in spatial interpolation settings. Here we propose, issuing probabilistic spatial predictions of precipitation using Light Gradient Boosting Machine (LightGBM). LightGBM is a boosting algorithm, highlighted by prize-winning entries in prediction and forecasting competitions. To assess LightGBM, we contribute a large-scale application that includes merging daily precipitation measurements in contiguous US with PERSIANN and GPM-IMERG satellite precipitation data. We focus on extreme quantiles of the probability distribution of the dependent variable, where LightGBM outperforms quantile regression forests (QRF, a variant of random forests) in terms of quantile score at extreme quantiles. Our study offers understanding of probabilistic predictions in spatial settings using machine learning.

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it. However, most of LGI methods assume to have a (noisy, incomplete, improvable, ...) input graph to rewire and can solely learn regular graph topologies. In the wake of the success of Topological Deep Learning (TDL), we study Latent Topology Inference (LTI) for learning higher-order cell complexes (with sparse and not regular topology) describing multi-way interactions between data points. To this aim, we introduce the Differentiable Cell Complex Module (DCM), a novel learnable function that computes cell probabilities in the complex to improve the downstream task. We show how to integrate DCM with cell complex message passing networks layers and train it in a end-to-end fashion, thanks to a two-step inference procedure that avoids an exhaustive search across all possible cells in the input, thus maintaining scalability. Our model is tested on several homophilic and heterophilic graph datasets and it is shown to outperform other state-of-the-art techniques, offering significant improvements especially in cases where an input graph is not provided.

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins.

This research paper delves into the integration of OpenAI's ChatGPT into embodied agent systems, evaluating its influence on interactive decision-making benchmark. Drawing a parallel to the concept of people assuming roles according to their unique strengths, we introduce InterAct. In this approach, we feed ChatGPT with varied prompts, assigning it a numerous roles like a checker and a sorter, then integrating them with the original language model. Our research shows a remarkable success rate of 98% in AlfWorld, which consists of 6 different tasks in a simulated household environment, emphasizing the significance of proficient prompt engineering. The results highlight ChatGPT's competence in comprehending and performing intricate tasks effectively in real-world settings, thus paving the way for further advancements in task planning.

The current work investigates the capability of Large language models (LLMs) that are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2) to predict psychiatric functioning from patient interviews and clinical descriptions without being trained to do so. To assess this, n = 145 depression and n =115 PTSD assessments and n = 46 clinical case studies across high prevalence/high comorbidity disorders (Depressive, Anxiety, Psychotic, trauma and stress, Addictive disorders) were analyzed using prompts to extract estimated clinical scores and diagnoses. Results demonstrate that Med-PaLM 2 is capable of assessing psychiatric functioning across a range of psychiatric conditions with the strongest performance being the prediction of depression scores based on standardized assessments (Accuracy range= 0.80 - 0.84) which were statistically indistinguishable from human clinical raters t(1,144) = 1.20; p = 0.23. Results show the potential for general clinical language models to flexibly predict psychiatric risk based on free descriptions of functioning from both patients and clinicians.

Mesh-based simulations play a key role when modeling complex physical systems that, in many disciplines across science and engineering, require the solution of parametrized time-dependent nonlinear partial differential equations (PDEs). In this context, full order models (FOMs), such as those relying on the finite element method, can reach high levels of accuracy, however often yielding intensive simulations to run. For this reason, surrogate models are developed to replace computationally expensive solvers with more efficient ones, which can strike favorable trade-offs between accuracy and efficiency. This work explores the potential usage of graph neural networks (GNNs) for the simulation of time-dependent PDEs in the presence of geometrical variability. In particular, we propose a systematic strategy to build surrogate models based on a data-driven time-stepping scheme where a GNN architecture is used to efficiently evolve the system. With respect to the majority of surrogate models, the proposed approach stands out for its ability of tackling problems with parameter dependent spatial domains, while simultaneously generalizing to different geometries and mesh resolutions. We assess the effectiveness of the proposed approach through a series of numerical experiments, involving both two- and three-dimensional problems, showing that GNNs can provide a valid alternative to traditional surrogate models in terms of computational efficiency and generalization to new scenarios. We also assess, from a numerical standpoint, the importance of using GNNs, rather than classical dense deep neural networks, for the proposed framework.

Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to those works exploiting low-rank label correlation. Note that multi-label is generally low-rank; low-rank label correlation is widely adopted in multi-label learning (MLL) literature. Inspired by that, we introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL. In such a way, low-rank label correlation is appropriately exploited in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL.

We study the online overlapping batch-means covariance estimator for Stochastic Gradient Descent (SGD) under Markovian sampling. We show that the convergence rates of the covariance estimator are $O\big(\sqrt{d}\,n^{-1/8}(\log n)^{1/4}\big)$ and $O\big(\sqrt{d}\,n^{-1/8}\big)$ under state-dependent and state-independent Markovian sampling, respectively, with $d$ representing dimensionality and $n$ denoting the number of observations or SGD iterations. Remarkably, these rates match the best-known convergence rate previously established for the independent and identically distributed ($\iid$) case by \cite{zhu2021online}, up to logarithmic factors. Our analysis overcomes significant challenges that arise due to Markovian sampling, leading to the introduction of additional error terms and complex dependencies between the blocks of the batch-means covariance estimator. Moreover, we establish the convergence rate for the first four moments of the $\ell_2$ norm of the error of SGD dynamics under state-dependent Markovian data, which holds potential interest as an independent result. To validate our theoretical findings, we provide numerical illustrations to derive confidence intervals for SGD when training linear and logistic regression models under Markovian sampling. Additionally, we apply our approach to tackle the intriguing problem of strategic classification with logistic regression, where adversaries can adaptively modify features during the training process to increase their chances of being classified in a specific target class.

We consider convex relaxations for recovering low-rank tensors based on constrained minimization over a ball induced by the tensor nuclear norm, recently introduced in \cite{tensor_tSVD}. We build on a recent line of results that considered convex relaxations for the recovery of low-rank matrices and established that under a strict complementarity condition (SC), both the convergence rate and per-iteration runtime of standard gradient methods may improve dramatically. We develop the appropriate strict complementarity condition for the tensor nuclear norm ball and obtain the following main results under this condition: 1. When the objective to minimize is of the form $f(\mX)=g(\mA\mX)+\langle{\mC,\mX}\rangle$ , where $g$ is strongly convex and $\mA$ is a linear map (e.g., least squares), a quadratic growth bound holds, which implies linear convergence rates for standard projected gradient methods, despite the fact that $f$ need not be strongly convex. 2. For a smooth objective function, when initialized in certain proximity of an optimal solution which satisfies SC, standard projected gradient methods only require SVD computations (for projecting onto the tensor nuclear norm ball) of rank that matches the tubal rank of the optimal solution. In particular, when the tubal rank is constant, this implies nearly linear (in the size of the tensor) runtime per iteration, as opposed to super linear without further assumptions. 3. For a nonsmooth objective function which admits a popular smooth saddle-point formulation, we derive similar results to the latter for the well known extragradient method. An additional contribution which may be of independent interest, is the rigorous extension of many basic results regarding tensors of arbitrary order, which were previously obtained only for third-order tensors.

In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We underline differences in terms of convergence rates between several unbiased compression operators, that all satisfy the same condition on their variance, thus going beyond the classical worst-case analysis. To do so, we focus on the case of least-squares regression (LSR) and analyze a general stochastic approximation algorithm for minimizing quadratic functions relying on a random field. We consider weak assumptions on the random field, tailored to the analysis (specifically, expected H\"older regularity), and on the noise covariance, enabling the analysis of various randomizing mechanisms, including compression. We then extend our results to the case of federated learning. More formally, we highlight the impact on the convergence of the covariance $\mathfrak{C}_{\mathrm{ania}}$ of the additive noise induced by the algorithm. We demonstrate despite the non-regularity of the stochastic field, that the limit variance term scales with $\mathrm{Tr}(\mathfrak{C}_{\mathrm{ania}} H^{-1})/K$ (where $H$ is the Hessian of the optimization problem and $K$ the number of iterations) generalizing the rate for the vanilla LSR case where it is $\sigma^2 \mathrm{Tr}(H H^{-1}) / K = \sigma^2 d / K$ (Bach and Moulines, 2013). Then, we analyze the dependency of $\mathfrak{C}_{\mathrm{ania}}$ on the compression strategy and ultimately its impact on convergence, first in the centralized case, then in two heterogeneous FL frameworks.

The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen and designed based on criteria which are often not clearly stated, for problem settings not clearly defined, tested in unrealistic settings, and/or in isolation from related approaches in the wider literature. This puts into question the potential for real-world impact of many approaches conceived in such contexts, and risks propagating a misguided research focus. We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks. Through and in reflection of this formulation and overview, helped by an informal survey of industrial players dealing with real-world data streams, we provide recommendations. Our main emphasis is that learning from data streams does not impose a single-pass or online-learning approach, or any particular learning regime; and any constraints on memory and time are not specific to streaming. Meanwhile, there exist established techniques for dealing with temporal dependence and concept drift, in other areas of the literature. For the data streams community, we thus encourage a shift in research focus, from dealing with often-artificial constraints and assumptions on the learning mode, to issues such as robustness, privacy, and interpretability which are increasingly relevant to learning in data streams in academic and industrial settings.

Despite the proliferation of diverse hardware accelerators (e.g., NPU, TPU, DPU), deploying deep learning models on edge devices with fixed-point hardware is still challenging due to complex model quantization and conversion. Existing model quantization frameworks like Tensorflow QAT [1], TFLite PTQ [2], and Qualcomm AIMET [3] supports only a limited set of quantization schemes (e.g., only asymmetric per-tensor quantization in TF1.x QAT [4]). Accordingly, deep learning models cannot be easily quantized for diverse fixed-point hardwares, mainly due to slightly different quantization requirements. In this paper, we envision a new type of model quantization approach called MRQ (model re-quantization), which takes existing quantized models and quickly transforms the models to meet different quantization requirements (e.g., asymmetric -> symmetric, non-power-of-2 scale -> power-of-2 scale). Re-quantization is much simpler than quantizing from scratch because it avoids costly re-training and provides support for multiple quantization schemes simultaneously. To minimize re-quantization error, we developed a new set of re-quantization algorithms including weight correction and rounding error folding. We have demonstrated that MobileNetV2 QAT model [7] can be quickly re-quantized into two different quantization schemes (i.e., symmetric and symmetric+power-of-2 scale) with less than 0.64 units of accuracy loss. We believe our work is the first to leverage this concept of re-quantization for model quantization and models obtained from the re-quantization process have been successfully deployed on NNA in the Echo Show devices.

Identification of nonlinear dynamical systems has been popularized by sparse identification of the nonlinear dynamics (SINDy) via the sequentially thresholded least squares (STLS) algorithm. Many extensions SINDy have emerged in the literature to deal with experimental data which are finite in length and noisy. Recently, the computationally intensive method of ensembling bootstrapped SINDy models (E-SINDy) was proposed for model identification, handling finite, highly noisy data. While the extensions of SINDy are numerous, their sparsity-promoting estimators occasionally provide sparse approximations of the dynamics as opposed to exact recovery. Furthermore, these estimators suffer under multicollinearity, e.g. the irrepresentable condition for the Lasso. In this paper, we demonstrate that the Trimmed Lasso for robust identification of models (TRIM) can provide exact recovery under more severe noise, finite data, and multicollinearity as opposed to E-SINDy. Additionally, the computational cost of TRIM is asymptotically equal to STLS since the sparsity parameter of the TRIM can be solved efficiently by convex solvers. We compare these methodologies on challenging nonlinear systems, specifically the Lorenz 63 system, the Bouc Wen oscillator from the nonlinear dynamics benchmark of No\"el and Schoukens, 2016, and a time delay system describing tool cutting dynamics. This study emphasizes the comparisons between STLS, reweighted $\ell_1$ minimization, and Trimmed Lasso in identification with respect to problems faced by practitioners: the problem of finite and noisy data, the performance of the sparse regression of when the library grows in dimension (multicollinearity), and automatic methods for choice of regularization parameters.

Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Statistical downscaling methods leveraging deep learning offer a solution to this problem by approximating local-scale climate fields from coarse variables, thus enabling regional GCM projections. Typically, climate fields of different variables of interest are downscaled independently, resulting in violations of fundamental physical properties across interconnected variables. This study investigates the scope of this problem and, through an application on temperature, lays the foundation for a framework introducing multi-variable hard constraints that guarantees physical relationships between groups of downscaled climate variables.

Are current language models capable of deception and lie detection? We study this question by introducing a text-based game called $\textit{Hoodwinked}$, inspired by $\textit{Mafia}$ and $\textit{Among Us}$. Players are locked in a house and must find a key to escape, but one player is tasked with killing the others. Each time a murder is committed, the surviving players have a natural language discussion then vote to banish one player from the game. We conduct experiments with agents controlled by GPT-3, GPT-3.5, and GPT-4 and find evidence of deception and lie detection capabilities. The killer often denies their crime and accuses others, leading to measurable effects on voting outcomes. More advanced models are more effective killers, outperforming smaller models in 18 of 24 pairwise comparisons. Secondary metrics provide evidence that this improvement is not mediated by different actions, but rather by stronger deception capabilities during discussions. Overall, we find substantial evidence that current language models are capable of deception. To better evaluate the ability of AI agents to deceive humans, we make this game publicly available at https://hoodwinked.ai/ .

Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quality "ahead-of-time" approximations. Undoubtedly, accurate indoor air quality approximation significantly helps provide a healthy indoor environment, optimize associated energy consumption, and offer human comfort. However, it is crucial to design an ML architecture to capture the domain knowledge, so-called problem physics. In this study, we propose six novel physics-based ML models for accurate indoor pollutant concentration approximations. The proposed models include an adroit combination of state-space concepts in physics, Gated Recurrent Units, and Decomposition techniques. The proposed models were illustrated using data collected from five offices in a commercial building in California. The proposed models are shown to be less complex, computationally more efficient, and more accurate than similar state-of-the-art transformer-based models. The superiority of the proposed models is due to their relatively light architecture (computational efficiency) and, more importantly, their ability to capture the underlying highly nonlinear patterns embedded in the often contaminated sensor-collected indoor air quality temporal data.

The diffusion model is capable of generating high-quality data through a probabilistic approach. However, it suffers from the drawback of slow generation speed due to the requirement of a large number of time steps. To address this limitation, recent models such as denoising diffusion implicit models (DDIM) focus on generating samples without directly modeling the probability distribution, while models like denoising diffusion generative adversarial networks (GAN) combine diffusion processes with GANs. In the field of speech synthesis, a recent diffusion speech synthesis model called DiffGAN-TTS, utilizing the structure of GANs, has been introduced and demonstrates superior performance in both speech quality and generation speed. In this paper, to further enhance the performance of DiffGAN-TTS, we propose a speech synthesis model with two discriminators: a diffusion discriminator for learning the distribution of the reverse process and a spectrogram discriminator for learning the distribution of the generated data. Objective metrics such as structural similarity index measure (SSIM), mel-cepstral distortion (MCD), F0 root mean squared error (F0 RMSE), short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), as well as subjective metrics like mean opinion score (MOS), are used to evaluate the performance of the proposed model. The evaluation results show that the proposed model outperforms recent state-of-the-art models such as FastSpeech2 and DiffGAN-TTS in various metrics. Our implementation and audio samples are located on GitHub.

In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Although exact unlearning can be achieved through complete model retraining using the remaining dataset, the associated computational costs have driven the development of efficient, approximate unlearning techniques. Moving beyond data-centric MU approaches, our study introduces a novel model-based perspective: model sparsification via weight pruning, which is capable of reducing the gap between exact unlearning and approximate unlearning. We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. This leads to a new MU paradigm, termed prune first, then unlearn, which infuses a sparse model prior into the unlearning process. Building on this insight, we also develop a sparsity-aware unlearning method that utilizes sparsity regularization to enhance the training process of approximate unlearning. Extensive experiments show that our proposals consistently benefit MU in various unlearning scenarios. A notable highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest unlearning methods) when using sparsity-aware unlearning. Furthermore, we demonstrate the practical impact of our proposed MU methods in addressing other machine learning challenges, such as defending against backdoor attacks and enhancing transfer learning. Codes are available at https://github.com/OPTML-Group/Unlearn-Sparse.

Job scheduling is a well-known Combinatorial Optimization problem with endless applications. Well planned schedules bring many benefits in the context of automated systems: among others, they limit production costs and waste. Nevertheless, the NP-hardness of this problem makes it essential to use heuristics whose design is difficult, requires specialized knowledge and often produces methods tailored to the specific task. This paper presents an original end-to-end Deep Reinforcement Learning approach to scheduling that automatically learns dispatching rules. Our technique is inspired by natural language encoder-decoder models for sequence processing and has never been used, to the best of our knowledge, for scheduling purposes. We applied and tested our method in particular to some benchmark instances of Job Shop Problem, but this technique is general enough to be potentially used to tackle other different optimal job scheduling tasks with minimal intervention. Results demonstrate that we outperform many classical approaches exploiting priority dispatching rules and show competitive results on state-of-the-art Deep Reinforcement Learning ones.

Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling the prior for initializations or using the prior distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we propose learning diffusion models as priors. We then can sample directly from the posterior trajectory distribution conditioned on task goals, by leveraging the inverse denoising process of diffusion models. Furthermore, diffusion has been recently shown to effectively encode data multimodality in high-dimensional settings, which is particularly well-suited for large trajectory dataset. To demonstrate our method efficacy, we compare our proposed method - Motion Planning Diffusion - against several baselines in simulated planar robot and 7-dof robot arm manipulator environments. To assess the generalization capabilities of our method, we test it in environments with previously unseen obstacles. Our experiments show that diffusion models are strong priors to encode high-dimensional trajectory distributions of robot motions.

One of the key objects of binary classification is the regression function, i.e., the conditional expectation of the class labels given the inputs. With the regression function not only a Bayes optimal classifier can be defined, but it also encodes the corresponding misclassification probabilities. The paper presents a resampling framework to construct exact, distribution-free and non-asymptotically guaranteed confidence regions for the true regression function for any user-chosen confidence level. Then, specific algorithms are suggested to demonstrate the framework. It is proved that the constructed confidence regions are strongly consistent, that is, any false model is excluded in the long run with probability one. The exclusion is quantified with probably approximately correct type bounds, as well. Finally, the algorithms are validated via numerical experiments, and the methods are compared to approximate asymptotic confidence ellipsoids.

$Q$ learning is a popular model free reinforcement learning method. Most of existing works focus on analyzing $Q$ learning for finite state and action spaces. If the state space is continuous, then the original $Q$ learning method can not be directly used. A modification of the original $Q$ learning method was proposed in (Shah and Xie, 2018), which estimates $Q$ values with nearest neighbors. Such modification makes $Q$ learning suitable for continuous state space. (Shah and Xie, 2018) shows that the convergence rate of estimated $Q$ function is $\tilde{O}(T^{-1/(d+3)})$, which is slower than the minimax lower bound $\tilde{\Omega}(T^{-1/(d+2)})$, indicating that this method is not efficient. This paper proposes two new $Q$ learning methods to bridge the gap of convergence rates in (Shah and Xie, 2018), with one of them being offline, while the other is online. Despite that we still use nearest neighbor approach to estimate $Q$ function, the algorithms are crucially different from (Shah and Xie, 2018). In particular, we replace the kernel nearest neighbor in discretized region with a direct nearest neighbor approach. Consequently, our approach significantly improves the convergence rate. Moreover, the time complexity is also significantly improved in high dimensional state spaces. Our analysis shows that both offline and online methods are minimax rate optimal.

Face swapping is a task that changes a facial identity of a given image to that of another person. In this work, we propose a novel face-swapping framework called Megapixel Facial Identity Manipulation (MFIM). The face-swapping model should achieve two goals. First, it should be able to generate a high-quality image. We argue that a model which is proficient in generating a megapixel image can achieve this goal. However, generating a megapixel image is generally difficult without careful model design. Therefore, our model exploits pretrained StyleGAN in the manner of GAN-inversion to effectively generate a megapixel image. Second, it should be able to effectively transform the identity of a given image. Specifically, it should be able to actively transform ID attributes (e.g., face shape and eyes) of a given image into those of another person, while preserving ID-irrelevant attributes (e.g., pose and expression). To achieve this goal, we exploit 3DMM that can capture various facial attributes. Specifically, we explicitly supervise our model to generate a face-swapped image with the desirable attributes using 3DMM. We show that our model achieves state-of-the-art performance through extensive experiments. Furthermore, we propose a new operation called ID mixing, which creates a new identity by semantically mixing the identities of several people. It allows the user to customize the new identity.

In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction matrices turn out to be a class of matrices which correspond to Hebbian kernels revised by iteratively applying some unlearning protocols. Remarkably, the number of unlearning steps is proved to be related to the regularization hyperparameters of the loss function and to the training time. Thus, we can design strategies to avoid overfitting that are formulated in terms of the algebraic properties of the interaction matrix, or, equivalently, in terms of regularization tuning and early-stopping strategies. The generalization capabilities of these attractor networks are also investigated: analytical results are obtained for random synthetic datasets, next, the emerging picture is corroborated by numerical experiments that highlight the existence of several regimes (i.e., overfitting, failure and success) as the dataset parameters are varied.

Human intelligence excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive intelligent models, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models, tools or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research platform designed for multi-step, real-world tasks. Specifically, OpenAGI uses a dual strategy, integrating standard benchmark tasks for benchmarking and evaluation, and open-ended tasks including more expandable models, tools or APIs for creative problem-solving. Tasks are presented as natural language queries to the LLM, which then selects and executes appropriate models. We also propose a Reinforcement Learning from Task Feedback (RLTF) mechanism that uses task results to improve the LLM's ability, which creates a self-improving AI feedback loop. While we acknowledge that AGI is a broad and multifaceted research challenge with no singularly defined solution path, the integration of LLMs with domain-specific expert models, inspired by mirroring the blend of general and specialized intelligence in humans, offers a promising approach towards AGI. We are open-sourcing the OpenAGI project's code, dataset, benchmarks, evaluation methods, and demo to foster community involvement in AGI advancement: https://github.com/agiresearch/OpenAGI.  ( 3 min )

Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers. More information and supplementary material can be found on our project webpage: https://motion-mode.github.io/  ( 3 min )

Emotions play an essential role in human communication. Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including robotics, digital behavioral healthcare, and media analytics. There are three types of emotional representations which are traditionally modeled in affective computing research: Action Units, Valence Arousal (VA), and Categorical Emotions. As part of an effort to move beyond these representations towards more fine-grained labels, we describe our submission to the newly introduced Emotional Reaction Intensity (ERI) Estimation challenge in the 5th competition for Affective Behavior Analysis in-the-Wild (ABAW). We developed four deep neural networks trained in the visual domain and a multimodal model trained with both visual and audio features to predict emotion reaction intensity. Our best performing model on the Hume-Reaction dataset achieved an average Pearson correlation coefficient of 0.4080 on the test set using a pre-trained ResNet50 model. This work provides a first step towards the development of production-grade models which predict emotion reaction intensities rather than discrete emotion categories.  ( 2 min )

Predictive variability due to data ambiguities has typically been addressed via construction of dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions. It is architecture agnostic and can be applied to any feed-forward deterministic network without changes to the architecture or training procedure. Experiments on regression tasks on imaging and non-imaging input data show the method's ability to generate diverse and multi-modal predictive distributions, and a desirable correlation of the estimated uncertainty with the prediction error.  ( 2 min )

Fine-tuning language models in a downstream task is the standard approach for many state-of-the-art methodologies in the field of NLP. However, when the distribution between the source task and target task drifts, \textit{e.g.}, conversational environments, these gains tend to be diminished. This article proposes a sequence of pre-training steps (a curriculum) guided by "data hacking" and grammar analysis that allows further gradual adaptation between pre-training distributions. In our experiments, we acquire a considerable improvement from our method compared to other known pre-training approaches for the MultiWoZ task.  ( 2 min )

Recently, bi-level optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML). Roughly speaking, BLO is a classical optimization problem that involves two levels of hierarchy (i.e., upper and lower levels), wherein obtaining the solution to the upper-level problem requires solving the lower-level one. BLO has become popular largely because it is powerful in modeling problems in SP and ML, among others, that involve optimizing nested objective functions. Prominent applications of BLO range from resource allocation for wireless systems to adversarial machine learning. In this work, we focus on a class of tractable BLO problems that often appear in SP and ML applications. We provide an overview of some basic concepts of this class of BLO problems, such as their optimality conditions, standard algorithms (including their optimization principles and practical implementations), as well as how they can be leveraged to obtain state-of-the-art results for a number of key SP and ML applications. Further, we discuss some recent advances in BLO theory, its implications for applications, and point out some limitations of the state-of-the-art that require significant future research efforts. Overall, we hope that this article can serve to accelerate the adoption of BLO as a generic tool to model, analyze, and innovate on a wide array of emerging SP and ML applications.  ( 3 min )

With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effective imputation models is a key topic in the research community. Recently, a major research direction is to employ neural network models such as self-organizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover interrelated features and common features simultaneously among data attributes. Especially, it is a very typical problem for classical autoencoders that they often learn invalid constant mappings, which dramatically hurts the filling performance. To solve the above-mentioned problems, we propose a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering that is incorporated into an iterative optimization process. This design can enhance the multi-dimensional feature fusion ability and thus improves the dynamic collaborative missing-value-filling performance. The effectiveness of the proposed model is validated by extensive experiments compared to a variety of baseline methods on thirteen data sets.  ( 2 min )

Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitful. Here, grounded on recent theory for Hebbian learning in soft winner-take-all networks, we present multilayer SoftHebb, i.e. an algorithm that trains deep neural networks, without any feedback, target, or error signals. As a result, it achieves efficiency by avoiding weight transport, non-local plasticity, time-locking of layer updates, iterative equilibria, and (self-) supervisory or other feedback signals -- which were necessary in other approaches. Its increased efficiency and biological compatibility do not trade off accuracy compared to state-of-the-art bio-plausible learning, but rather improve it. With up to five hidden layers and an added linear classifier, accuracies on MNIST, CIFAR-10, STL-10, and ImageNet, respectively reach 99.4%, 80.3%, 76.2%, and 27.3%. In conclusion, SoftHebb shows with a radically different approach from BP that Deep Learning over few layers may be plausible in the brain and increases the accuracy of bio-plausible machine learning. Code is available at https://github.com/NeuromorphicComputing/SoftHebb.  ( 2 min )

Real-time rendering for video games has become increasingly challenging due to the need for higher resolutions, framerates and photorealism. Supersampling has emerged as an effective solution to address this challenge. Our work introduces a novel neural algorithm for supersampling rendered content that is 4 times more efficient than existing methods while maintaining the same level of accuracy. Additionally, we introduce a new dataset which provides auxiliary modalities such as motion vectors and depth generated using graphics rendering features like viewport jittering and mipmap biasing at different resolutions. We believe that this dataset fills a gap in the current dataset landscape and can serve as a valuable resource to help measure progress in the field and advance the state-of-the-art in super-resolution techniques for gaming content.  ( 2 min )

We present a new algorithm for automatically bounding the Taylor remainder series. In the special case of a scalar function $f: \mathbb{R} \to \mathbb{R}$, our algorithm takes as input a reference point $x_0$, trust region $[a, b]$, and integer $k \ge 1$, and returns an interval $I$ such that $f(x) - \sum_{i=0}^{k-1} \frac {1} {i!} f^{(i)}(x_0) (x - x_0)^i \in I (x - x_0)^k$ for all $x \in [a, b]$. As in automatic differentiation, the function $f$ is provided to the algorithm in symbolic form, and must be composed of known atomic functions. At a high level, our algorithm has two steps. First, for a variety of commonly-used elementary functions (e.g., $\exp$, $\log$), we use recently-developed theory to derive sharp polynomial upper and lower bounds on the Taylor remainder series. We then recursively combine the bounds for the elementary functions using an interval arithmetic variant of Taylor-mode automatic differentiation. Our algorithm can make efficient use of machine learning hardware accelerators, and we provide an open source implementation in JAX. We then turn our attention to applications. Most notably, in a companion paper we use our new machinery to create the first universal majorization-minimization optimization algorithms: algorithms that iteratively minimize an arbitrary loss using a majorizer that is derived automatically, rather than by hand. We also show that our automatically-derived bounds can be used for verified global optimization and numerical integration, and to prove sharper versions of Jensen's inequality.  ( 3 min )

With the advancement of DNNs into safety-critical applications, testing approaches for such models have gained more attention. A current direction is the search for and identification of systematic weaknesses that put safety assumptions based on average performance values at risk. Such weaknesses can take on the form of (semantically coherent) subsets or areas in the input space where a DNN performs systematically worse than its expected average. However, it is non-trivial to attribute the reason for such observed low performances to the specific semantic features that describe the subset. For instance, inhomogeneities within the data w.r.t. other (non-considered) attributes might distort results. However, taking into account all (available) attributes and their interaction is often computationally highly expensive. Inspired by counterfactual explanations, we propose an effective and computationally cheap algorithm to validate the semantic attribution of existing subsets, i.e., to check whether the identified attribute is likely to have caused the degraded performance. We demonstrate this approach on an example from the autonomous driving domain using highly annotated simulated data, where we show for a semantic segmentation model that (i) performance differences among the different pedestrian assets exist, but (ii) only in some cases is the asset type itself the reason for this reduction in the performance.  ( 2 min )

ChatMOF is an autonomous Artificial Intelligence (AI) system that is built to predict and generate of metal-organic frameworks (MOFs). By leveraging a large-scale language model (gpt-3.5-turbo), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid structured queries. The system is comprised of three core components (i.e. an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generation. The study further explores the merits and constraints of using large language models (LLMs) AI system in material sciences using and showcases its transformative potential for future advancements.  ( 2 min )

To interact with humans in the world, agents need to understand the diverse types of language that people use, relate them to the visual world, and act based on them. While current agents learn to execute simple language instructions from task rewards, we aim to build agents that leverage diverse language that conveys general knowledge, describes the state of the world, provides interactive feedback, and more. Our key idea is that language helps agents predict the future: what will be observed, how the world will behave, and which situations will be rewarded. This perspective unifies language understanding with future prediction as a powerful self-supervised learning objective. We present Dynalang, an agent that learns a multimodal world model that predicts future text and image representations and learns to act from imagined model rollouts. Unlike traditional agents that use language only to predict actions, Dynalang acquires rich language understanding by using past language also to predict future language, video, and rewards. In addition to learning from online interaction in an environment, Dynalang can be pretrained on datasets of text, video, or both without actions or rewards. From using language hints in grid worlds to navigating photorealistic scans of homes, Dynalang utilizes diverse types of language to improve task performance, including environment descriptions, game rules, and instructions.  ( 2 min )

Multi-label image recognition in the low-label regime is a task of great challenge and practical significance. Previous works have focused on learning the alignment between textual and visual spaces to compensate for limited image labels, yet may suffer from reduced accuracy due to the scarcity of high-quality multi-label annotations. In this research, we leverage the powerful alignment between textual and visual features pretrained with millions of auxiliary image-text pairs. We introduce an efficient and effective framework called Evidence-guided Dual Context Optimization (DualCoOp++), which serves as a unified approach for addressing partial-label and zero-shot multi-label recognition. In DualCoOp++ we separately encode evidential, positive, and negative contexts for target classes as parametric components of the linguistic input (i.e., prompts). The evidential context aims to discover all the related visual content for the target class, and serves as guidance to aggregate positive and negative contexts from the spatial domain of the image, enabling better distinguishment between similar categories. Additionally, we introduce a Winner-Take-All module that promotes inter-class interaction during training, while avoiding the need for extra parameters and costs. As DualCoOp++ imposes minimal additional learnable overhead on the pretrained vision-language framework, it enables rapid adaptation to multi-label recognition tasks with limited annotations and even unseen classes. Experiments on standard multi-label recognition benchmarks across two challenging low-label settings demonstrate the superior performance of our approach compared to state-of-the-art methods.  ( 3 min )

Nowadays, autonomous cars are gaining traction due to their numerous potential applications on battlefields and in resolving a variety of other real-world challenges. The main goal of our project is to build an autonomous system using DeepRacer which will follow a specific person (for our project, a soldier) when they will be moving in any direction. Two main components to accomplish this project is an optimized Single-Shot Multibox Detection (SSD) object detection model and a Reinforcement Learning (RL) model. We accomplished the task using SSD Lite instead of SSD and at the end, compared the results among SSD, SSD with Neural Computing Stick (NCS), and SSD Lite. Experimental results show that SSD Lite gives better performance among these three techniques and exhibits a considerable boost in inference speed (~2-3 times) without compromising accuracy.  ( 2 min )

Continual learning seeks to enable deep learners to train on a series of tasks of unknown length without suffering from the catastrophic forgetting of previous tasks. One effective solution is replay, which involves storing few previous experiences in memory and replaying them when learning the current task. However, there is still room for improvement when it comes to selecting the most informative samples for storage and determining the optimal number of samples to be stored. This study aims to address these issues with a novel comparison of the commonly used reservoir sampling to various alternative population strategies and providing a novel detailed analysis of how to find the optimal number of stored samples.  ( 2 min )

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.  ( 3 min )

New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven-discoveries.  ( 2 min )

Machine learning models are known to be vulnerable to adversarial evasion attacks as illustrated by image classification models. Thoroughly understanding such attacks is critical in order to ensure the safety and robustness of critical AI tasks. However, most evasion attacks are difficult to deploy against a majority of AI systems because they have focused on image domain with only few constraints. An image is composed of homogeneous, numerical, continuous, and independent features, unlike many other input types to AI systems used in practice. Furthermore, some input types include additional semantic and functional constraints that must be observed to generate realistic adversarial inputs. In this work, we propose a new framework to enable the generation of adversarial inputs irrespective of the input type and task domain. Given an input and a set of pre-defined input transformations, our framework discovers a sequence of transformations that result in a semantically correct and functional adversarial input. We demonstrate the generality of our approach on several diverse machine learning tasks with various input representations. We also show the importance of generating adversarial examples as they enable the deployment of mitigation techniques.  ( 2 min )

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their loglikelihood on a test set. Other methods output a prediction interval directly. These methods are often tested by examining the fraction of test points that fall inside the corresponding prediction intervals. Intuitively both approaches seem logical. However, we demonstrate through both theoretical arguments and simulations that both ways of evaluating the quality of uncertainty estimates have serious flaws. Firstly, both approaches cannot disentangle the separate components that jointly create the predictive uncertainty, making it difficult to evaluate the quality of the estimates of these components. Secondly, a better loglikelihood does not guarantee better prediction intervals, which is what the methods are often used for in practice. Moreover, the current approach to test prediction intervals directly has additional flaws. We show why it is fundamentally flawed to test a prediction or confidence interval on a single test set. At best, marginal coverage is measured, implicitly averaging out overconfident and underconfident predictions. A much more desirable property is pointwise coverage, requiring the correct coverage for each prediction. We demonstrate through practical examples that these effects can result in favoring a method, based on the predictive uncertainty, that has undesirable behaviour of the confidence or prediction intervals. Finally, we propose a simulation-based testing approach that addresses these problems while still allowing easy comparison between different methods.  ( 3 min )

We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the Combined Actuarial Neural Network (CANN) framework proposed by Mario W\"uthrich and Michael Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part. The MLP part is used to process telematics car driving data given as a vector characterizing the driving behavior of each insured driver. In addition to the Poisson and negative binomial distributions for cross-sectional data, we propose a procedure for training our CANN model with a multivariate negative binomial (MVNB) specification. By doing so, we introduce a longitudinal model that accounts for the dependence between contracts from the same insured. Our results reveal that the CANN models exhibit superior performance compared to log-linear models that rely on manually engineered telematics features.

Single-cell data integration can provide a comprehensive molecular view of cells, and many algorithms have been developed to remove unwanted technical or biological variations and integrate heterogeneous single-cell datasets. Despite their wide usage, existing methods suffer from several fundamental limitations. In particular, we lack a rigorous statistical test for whether two high-dimensional single-cell datasets are alignable (and therefore should even be aligned). Moreover, popular methods can substantially distort the data during alignment, making the aligned data and downstream analysis difficult to interpret. To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data. SMAI provides a statistical test to robustly determine the alignability between datasets to avoid misleading inference, and is justified by high-dimensional statistical theory. On a diverse range of real and simulated benchmark datasets, it outperforms commonly used alignment methods. Moreover, we show that SMAI improves various downstream analyses such as identification of differentially expressed genes and imputation of single-cell spatial transcriptomics, providing further biological insights. SMAI's interpretability also enables quantification and a deeper understanding of the sources of technical confounders in single-cell data.

This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building block for uncertainty estimation methods in a regression setting, for instance Concrete dropout (Gal et al., 2017) and Deep Ensembles (Lakshminarayanan et al., 2017). Specifically, an MVE network assumes that the data is produced from a normal distribution with a mean function and variance function. The MVE network outputs a mean and variance estimate and optimizes the network parameters by minimizing the negative loglikelihood. In our paper, we present two significant insights. Firstly, the convergence difficulties reported in recent work can be relatively easily prevented by following the simple yet often overlooked recommendation from the original authors that a warm-up period should be used. During this period, only the mean is optimized with a fixed variance. We demonstrate the effectiveness of this step through experimentation, highlighting that it should be standard practice. As a sidenote, we examine whether, after the warm-up, it is beneficial to fix the mean while optimizing the variance or to optimize both simultaneously. Here, we do not observe a substantial difference. Secondly, we introduce a novel improvement of the MVE network: separate regularization of the mean and the variance estimate. We demonstrate, both on toy examples and on a number of benchmark UCI regression data sets, that following the original recommendations and the novel separate regularization can lead to significant improvements.

An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate their compatibility with anatomical and physiological observations. Similarity matching objectives have served as successful starting points for deriving online algorithms that map onto neural networks (NNs) with point neurons and Hebbian/anti-Hebbian plasticity. These NN models account for many anatomical and physiological observations; however, the objectives have limited computational power and the derived NNs do not explain multi-compartmental neuronal structures and non-Hebbian forms of plasticity that are prevalent throughout the brain. In this article, we unify and generalize recent extensions of the similarity matching approach to address more complex objectives, including a large class of unsupervised and self-supervised learning tasks that can be formulated as symmetric generalized eigenvalue problems or nonnegative matrix factorization problems. Interestingly, the online algorithms derived from these objectives naturally map onto NNs with multi-compartmental neurons and local, non-Hebbian learning rules. Therefore, this unified extension of the similarity matching approach provides a normative framework that facilitates understanding multi-compartmental neuronal structures and non-Hebbian plasticity found throughout the brain.

We study the online overlapping batch-means covariance estimator for Stochastic Gradient Descent (SGD) under Markovian sampling. We show that the convergence rates of the covariance estimator are $O\big(\sqrt{d}\,n^{-1/8}(\log n)^{1/4}\big)$ and $O\big(\sqrt{d}\,n^{-1/8}\big)$ under state-dependent and state-independent Markovian sampling, respectively, with $d$ representing dimensionality and $n$ denoting the number of observations or SGD iterations. Remarkably, these rates match the best-known convergence rate previously established for the independent and identically distributed ($\iid$) case by \cite{zhu2021online}, up to logarithmic factors. Our analysis overcomes significant challenges that arise due to Markovian sampling, leading to the introduction of additional error terms and complex dependencies between the blocks of the batch-means covariance estimator. Moreover, we establish the convergence rate for the first four moments of the $\ell_2$ norm of the error of SGD dynamics under state-dependent Markovian data, which holds potential interest as an independent result. To validate our theoretical findings, we provide numerical illustrations to derive confidence intervals for SGD when training linear and logistic regression models under Markovian sampling. Additionally, we apply our approach to tackle the intriguing problem of strategic classification with logistic regression, where adversaries can adaptively modify features during the training process to increase their chances of being classified in a specific target class.

$Q$ learning is a popular model free reinforcement learning method. Most of existing works focus on analyzing $Q$ learning for finite state and action spaces. If the state space is continuous, then the original $Q$ learning method can not be directly used. A modification of the original $Q$ learning method was proposed in (Shah and Xie, 2018), which estimates $Q$ values with nearest neighbors. Such modification makes $Q$ learning suitable for continuous state space. (Shah and Xie, 2018) shows that the convergence rate of estimated $Q$ function is $\tilde{O}(T^{-1/(d+3)})$, which is slower than the minimax lower bound $\tilde{\Omega}(T^{-1/(d+2)})$, indicating that this method is not efficient. This paper proposes two new $Q$ learning methods to bridge the gap of convergence rates in (Shah and Xie, 2018), with one of them being offline, while the other is online. Despite that we still use nearest neighbor approach to estimate $Q$ function, the algorithms are crucially different from (Shah and Xie, 2018). In particular, we replace the kernel nearest neighbor in discretized region with a direct nearest neighbor approach. Consequently, our approach significantly improves the convergence rate. Moreover, the time complexity is also significantly improved in high dimensional state spaces. Our analysis shows that both offline and online methods are minimax rate optimal.

We introduce a novel interpretable tree based algorithm for prediction in a regression setting. Our motivation is to estimate the unknown regression function from a functional decomposition perspective in which the functional components correspond to lower order interaction terms. The idea is to modify the random forest algorithm by keeping certain leaves after they are split instead of deleting them. This leads to non-binary trees which we refer to as planted trees. An extension to a forest leads to our random planted forest algorithm. Additionally, the maximum number of covariates which can interact within a leaf can be bounded. If we set this interaction bound to one, the resulting estimator is a sum of one-dimensional functions. In the other extreme case, if we do not set a limit, the resulting estimator and corresponding model place no restrictions on the form of the regression function. In a simulation study we find encouraging prediction and visualisation properties of our random planted forest method. We also develop theory for an idealized version of random planted forests in cases where the interaction bound is low. We show that if it is smaller than three, the idealized version achieves asymptotically optimal convergence rates up to a logarithmic factor. Code is available on GitHub https://github.com/PlantedML/randomPlantedForest.

Disordered many-body systems exhibit a wide range of emergent phenomena across different scales. These complex behaviors can be utilized for various information processing tasks such as error correction, learning, and optimization. Despite the empirical success of utilizing these systems for intelligent tasks, the underlying principles that govern their emergent intelligent behaviors remain largely unknown. In this thesis, we aim to characterize such emergent intelligence in disordered systems through statistical physics. We chart a roadmap for our efforts in this thesis based on two axes: learning mechanisms (long-term memory vs. working memory) and learning dynamics (artificial vs. natural). Throughout our journey, we uncover relationships between learning mechanisms and physical dynamics that could serve as guiding principles for designing intelligent systems. We hope that our investigation into the emergent intelligence of seemingly disparate learning systems can expand our current understanding of intelligence beyond neural systems and uncover a wider range of computational substrates suitable for AI applications.

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive efforts on improving both empirical and provable robustness against evasion attacks; however, the provable robustness against backdoor attacks still remains largely unexplored. In this paper, we focus on certifying the machine learning model robustness against general threat models, especially backdoor attacks. We first provide a unified framework via randomized smoothing techniques and show how it can be instantiated to certify the robustness against both evasion and backdoor attacks. We then propose the first robust training process, RAB, to smooth the trained model and certify its robustness against backdoor attacks. We prove the robustness bound for machine learning models trained with RAB and prove that our robustness bound is tight. In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm that eliminates the need to sample from a noise distribution for such models. Empirically, we conduct comprehensive experiments for different machine learning (ML) models such as DNNs, support vector machines, and K-NN models on MNIST, CIFAR-10, and ImageNette datasets and provide the first benchmark for certified robustness against backdoor attacks. In addition, we evaluate K-NN models on a spambase tabular dataset to demonstrate the advantages of the proposed exact algorithm. Both the theoretic analysis and the comprehensive evaluation on diverse ML models and datasets shed light on further robust learning strategies against general training time attacks.

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their loglikelihood on a test set. Other methods output a prediction interval directly. These methods are often tested by examining the fraction of test points that fall inside the corresponding prediction intervals. Intuitively both approaches seem logical. However, we demonstrate through both theoretical arguments and simulations that both ways of evaluating the quality of uncertainty estimates have serious flaws. Firstly, both approaches cannot disentangle the separate components that jointly create the predictive uncertainty, making it difficult to evaluate the quality of the estimates of these components. Secondly, a better loglikelihood does not guarantee better prediction intervals, which is what the methods are often used for in practice. Moreover, the current approach to test prediction intervals directly has additional flaws. We show why it is fundamentally flawed to test a prediction or confidence interval on a single test set. At best, marginal coverage is measured, implicitly averaging out overconfident and underconfident predictions. A much more desirable property is pointwise coverage, requiring the correct coverage for each prediction. We demonstrate through practical examples that these effects can result in favoring a method, based on the predictive uncertainty, that has undesirable behaviour of the confidence or prediction intervals. Finally, we propose a simulation-based testing approach that addresses these problems while still allowing easy comparison between different methods.

Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as racial biases. Causal thinking is needed for data-driven decisions. Here, we give an introduction to the key elements, focusing on routinely-collected data, electronic health records (EHRs) and claims data. Using such data to assess the value of an intervention requires care: temporal dependencies and existing practices easily confound the causal effect. We present a step-by-step framework to help build valid decision making from real-life patient records by emulating a randomized trial before individualizing decisions, eg with machine learning. Our framework highlights the most important pitfalls and considerations in analysing EHRs or claims data to draw causal conclusions. We illustrate the various choices in studying the effect of albumin on sepsis mortality in the Medical Information Mart for Intensive Care database (MIMIC-IV). We study the impact of various choices at every step, from feature extraction to causal-estimator selection. In a tutorial spirit, the code and the data are openly available.

In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We underline differences in terms of convergence rates between several unbiased compression operators, that all satisfy the same condition on their variance, thus going beyond the classical worst-case analysis. To do so, we focus on the case of least-squares regression (LSR) and analyze a general stochastic approximation algorithm for minimizing quadratic functions relying on a random field. We consider weak assumptions on the random field, tailored to the analysis (specifically, expected H\"older regularity), and on the noise covariance, enabling the analysis of various randomizing mechanisms, including compression. We then extend our results to the case of federated learning. More formally, we highlight the impact on the convergence of the covariance $\mathfrak{C}_{\mathrm{ania}}$ of the additive noise induced by the algorithm. We demonstrate despite the non-regularity of the stochastic field, that the limit variance term scales with $\mathrm{Tr}(\mathfrak{C}_{\mathrm{ania}} H^{-1})/K$ (where $H$ is the Hessian of the optimization problem and $K$ the number of iterations) generalizing the rate for the vanilla LSR case where it is $\sigma^2 \mathrm{Tr}(H H^{-1}) / K = \sigma^2 d / K$ (Bach and Moulines, 2013). Then, we analyze the dependency of $\mathfrak{C}_{\mathrm{ania}}$ on the compression strategy and ultimately its impact on convergence, first in the centralized case, then in two heterogeneous FL frameworks.

In recent years, multiple notions of algorithmic fairness have arisen. One such notion is individual fairness (IF), which requires that individuals who are similar receive similar treatment. In parallel, matrix estimation (ME) has emerged as a natural paradigm for handling noisy data with missing values. In this work, we connect the two concepts. We show that pre-processing data using ME can improve an algorithm's IF without sacrificing performance. Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions. We then show that, under analogous conditions, SVT pre-processing also yields estimates that are consistent and approximately minimax optimal. As such, the ME pre-processing step does not, under the stated conditions, increase the prediction error of the base algorithm, i.e., does not impose a fairness-performance trade-off. We verify these results on synthetic and real data.

We consider the degree-Rips construction from topological data analysis, which provides a density-sensitive, multiparameter hierarchical clustering algorithm. We analyze its stability to perturbations of the input data using the correspondence-interleaving distance, a metric for hierarchical clusterings that we introduce. Taking certain one-parameter slices of degree-Rips recovers well-known methods for density-based clustering, but we show that these methods are unstable. However, we prove that degree-Rips, as a multiparameter object, is stable, and we propose an alternative approach for taking slices of degree-Rips, which yields a one-parameter hierarchical clustering algorithm with better stability properties. We prove that this algorithm is consistent, using the correspondence-interleaving distance. We provide an algorithm for extracting a single clustering from one-parameter hierarchical clusterings, which is stable with respect to the correspondence-interleaving distance. And, we integrate these methods into a pipeline for density-based clustering, which we call Persistable. Adapting tools from multiparameter persistent homology, we propose visualization tools that guide the selection of all parameters of the pipeline. We demonstrate Persistable on benchmark datasets, showing that it identifies multi-scale cluster structure in data.

We consider convex relaxations for recovering low-rank tensors based on constrained minimization over a ball induced by the tensor nuclear norm, recently introduced in \cite{tensor_tSVD}. We build on a recent line of results that considered convex relaxations for the recovery of low-rank matrices and established that under a strict complementarity condition (SC), both the convergence rate and per-iteration runtime of standard gradient methods may improve dramatically. We develop the appropriate strict complementarity condition for the tensor nuclear norm ball and obtain the following main results under this condition: 1. When the objective to minimize is of the form $f(\mX)=g(\mA\mX)+\langle{\mC,\mX}\rangle$ , where $g$ is strongly convex and $\mA$ is a linear map (e.g., least squares), a quadratic growth bound holds, which implies linear convergence rates for standard projected gradient methods, despite the fact that $f$ need not be strongly convex. 2. For a smooth objective function, when initialized in certain proximity of an optimal solution which satisfies SC, standard projected gradient methods only require SVD computations (for projecting onto the tensor nuclear norm ball) of rank that matches the tubal rank of the optimal solution. In particular, when the tubal rank is constant, this implies nearly linear (in the size of the tensor) runtime per iteration, as opposed to super linear without further assumptions. 3. For a nonsmooth objective function which admits a popular smooth saddle-point formulation, we derive similar results to the latter for the well known extragradient method. An additional contribution which may be of independent interest, is the rigorous extension of many basic results regarding tensors of arbitrary order, which were previously obtained only for third-order tensors.

Algorithms for solving the linear classification problem have a long history, dating back at least to 1936 with linear discriminant analysis. For linearly separable data, many algorithms can obtain the exact solution to the corresponding 0-1 loss classification problem efficiently, but for data which is not linearly separable, it has been shown that this problem, in full generality, is NP-hard. Alternative approaches all involve approximations of some kind, including the use of surrogates for the 0-1 loss (for example, the hinge or logistic loss) or approximate combinatorial search, none of which can be guaranteed to solve the problem exactly. Finding efficient algorithms to obtain an exact i.e. globally optimal solution for the 0-1 loss linear classification problem with fixed dimension, remains an open problem. In research we report here, we detail the rigorous construction of a new algorithm, incremental cell enumeration (ICE), that can solve the 0-1 loss classification problem exactly in polynomial time. We prove correctness using concepts from the theory of hyperplane arrangements and oriented matroids. We demonstrate the effectiveness of this algorithm on synthetic and real-world datasets, showing optimal accuracy both in and out-of-sample, in practical computational time. We also empirically demonstrate how the use of approximate upper bound leads to polynomial time run-time improvements to the algorithm whilst retaining exactness. To our knowledge, this is the first, rigorously-proven polynomial time, practical algorithm for this long-standing problem.

This paper introduces two randomized preconditioning techniques for robustly solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$). The first method, RPCholesky preconditioning, is capable of accurately solving the full-data KRR problem in $O(N^2)$ arithmetic operations, assuming sufficiently rapid polynomial decay of the kernel matrix eigenvalues. The second method, KRILL preconditioning, offers an accurate solution to a restricted version of the KRR problem involving $k \ll N$ selected data centers at a cost of $O((N + k^2) k \log k)$ operations. The proposed methods solve a broad range of KRR problems and overcome the failure modes of previous KRR preconditioners, making them ideal for practical applications.  ( 2 min )

One of the key objects of binary classification is the regression function, i.e., the conditional expectation of the class labels given the inputs. With the regression function not only a Bayes optimal classifier can be defined, but it also encodes the corresponding misclassification probabilities. The paper presents a resampling framework to construct exact, distribution-free and non-asymptotically guaranteed confidence regions for the true regression function for any user-chosen confidence level. Then, specific algorithms are suggested to demonstrate the framework. It is proved that the constructed confidence regions are strongly consistent, that is, any false model is excluded in the long run with probability one. The exclusion is quantified with probably approximately correct type bounds, as well. Finally, the algorithms are validated via numerical experiments, and the methods are compared to approximate asymptotic confidence ellipsoids.  ( 2 min )

Distribution shifts are a serious concern in modern statistical learning as they can systematically change the properties of the data away from the truth. We focus on Wasserstein distribution shifts, where every data point may undergo a slight perturbation, as opposed to the Huber contamination model where a fraction of observations are outliers. We formulate and study shifts beyond independent perturbations, exploring Joint Distribution Shifts, where the per-observation perturbations can be coordinated. We analyze several important statistical problems, including location estimation, linear regression, and non-parametric density estimation. Under a squared loss for mean estimation and prediction error in linear regression, we find the exact minimax risk, a least favorable perturbation, and show that the sample mean and least squares estimators are respectively optimal. This holds for both independent and joint shifts, but the least favorable perturbations and minimax risks differ. For other problems, we provide nearly optimal estimators and precise finite-sample bounds. We also introduce several tools for bounding the minimax risk under distribution shift, such as a smoothing technique for location families, and generalizations of classical tools including least favorable sequences of priors, the modulus of continuity, Le Cam's, Fano's, and Assouad's methods.  ( 2 min )

Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too vague to express with a single prior, an alternative approach is needed. Gamma-minimax estimators provide such an approach. These estimators minimize the worst-case Bayes risk over a set $\Gamma$ of prior distributions that are compatible with the available knowledge. Traditionally, Gamma-minimaxity is defined for parametric models. In this work, we define Gamma-minimax estimators for general models and propose adversarial meta-learning algorithms to compute them when the set of prior distributions is constrained by generalized moments. Accompanying convergence guarantees are also provided. We also introduce a neural network class that provides a rich, but finite-dimensional, class of estimators from which a Gamma-minimax estimator can be selected. We illustrate our method in two settings, namely entropy estimation and a prediction problem that arises in biodiversity studies.  ( 2 min )

New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven-discoveries.  ( 2 min )

With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. One of the most prominent uncertainty estimation methods is Deep Ensembles (Lakshminarayanan et al., 2017) . A classical parametric model has uncertainty in the parameters due to the fact that the data on which the model is build is a random sample. A modern neural network has an additional uncertainty component since the optimization of the network is random. Lakshminarayanan et al. (2017) noted that Deep Ensembles do not incorporate the classical uncertainty induced by the effect of finite data. In this paper, we present a computationally cheap extension of Deep Ensembles for the regression setting, called Bootstrapped Deep Ensembles, that explicitly takes this classical effect of finite data into account using a modified version of the parametric bootstrap. We demonstrate through an experimental study that our method significantly improves upon standard Deep Ensembles  ( 2 min )

An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.  ( 2 min )

Anyone know of a cloud service renting on-demand GPU instances (RTX 4090 preferably) that run Windows 10 or newer? Believe me, I know... I rent on-demand instances from vast.ai for Linux and have been exceedingly happy with their services. I've also used paperspace in the past with good success. Unfortunately, we are in need of RTX 4090s (or roughly equivalent performing Tesla cards) that run on a host OS of Windows 10+ (Server/Win11/etc all fine) because a lot of the modeling software in the industry I work in runs on Windows-only, which is absurd, but nevertheless the truth. The fastest I can find are A6000s on paperspace which won't cut the mustard. At the moment we have a 3090 and a bunch of 3070s on-prem which are doing OK but the RTX 4090 is simply much much better, and unsurprisingly the Windows-only software is also not coded in a way that takes advantage of multiple GPUs all that well either. Thanks for any help or referrals provided, I really appreciate it. (Have checked paperspace, vastai, runpod, and a few other smaller ones to no avail) submitted by /u/kyleboddy [link] [comments]  ( 9 min )

Hello everyone. I am new to this sub so please go easy on me lol. I want to implement a neural net that recognizes whether an object in an image matches one of a set of objects with limited training data. I already have worked on a siamese network implementation with triplet loss and ResNet, but I am not getting great performance. Should I do something else? For extra info, there are roughly 300 objects/classes and around 7 images per object (most are augmented images) submitted by /u/Nearby_Ad_5644 [link] [comments]  ( 8 min )

Paper: https://arxiv.org/abs/2308.01399 Github: https://github.com/jlin816/dynalang Code coming soon! Abstract: To interact with humans in the world, agents need to understand the diverse types of language that people use, relate them to the visual world, and act based on them. While current agents learn to execute simple language instructions from task rewards, we aim to build agents that leverage diverse language that conveys general knowledge, describes the state of the world, provides interactive feedback, and more. Our key idea is that language helps agents predict the future: what will be observed, how the world will behave, and which situations will be rewarded. This perspective unifies language understanding with future prediction as a powerful self-supervised learning object…  ( 9 min )

https://preview.redd.it/p70yknwm15gb1.png?width=1901&format=png&auto=webp&s=7417914304cc23d6691653cd73396bd600a44b0a Hey Guys, I am happy to share with you a web application I've working on the past couple weeks. It's a tool to predict the outcome of soccer games in minor football leagues. Named ScoreCast, it predicts the outcome of soccer games in six minor leagues: Serie A Brazil, Serie B Brazil, Primera Division Argentina, J1 League Japan, Eliteserien Norway, and Veikkausliiga Finland. Since I am really interested in football analytics and also not being able to find many online tools for predicting the outcomes in minor soccer leagues, I had the need to create ScoreCast to have it as a tool for guidance on this field. If you want to check it out, here are some links that might help: Github: https://github.com/Costasgk/ScoreCast The App: https://score-cast-3a6cb8fe5c50.herokuapp.com/ Medium: https://medium.com/@costascg9/scorecast-a-tool-for-predicting-football-game-outcomes-in-minor-leagues-666f7acca3a Thank you for your time! submitted by /u/Costas_8 [link] [comments]  ( 9 min )

I'm working on a Python project that aims to transform phone-quality acoustic guitar recordings into studio-like ones. My approach involves using a Generative Adversarial Network (GAN) with two components: a Generator and a Discriminator. Here's a quick rundown of my process: Data Loading & Preprocessing: Convert acoustic guitar recordings to spectrograms and split into training and validation sets. Generator: Neural network trained to create high-quality studio recording spectrograms from low-quality inputs. Discriminator: Another neural network trained to differentiate between real and generator-created high-quality spectrograms. Training: Train the Generator and Discriminator against each other in a cat-and-mouse game of deception and detection. Audio Enhancement: Feed the Generator a low-quality spectrogram, get a high-quality one out, and convert it back into an audio file. I'm reaching out because I'm not entirely satisfied with the quality of the output. The enhanced audio is just rhythmic noise, what am i missing with generating the audio? I'm wondering if anyone here has experience with GANs for audio enhancement and can offer some advice. Is there something I might be missing in my approach? Are there any tips or tricks you've found helpful in your own work? And yes, I'm prepared for you to tear me a new one. Bring on the constructive criticism! git repo: https://github.com/Gabeiscool420/AURAL_GAN-predictive_model/blob/main/requirements.txt submitted by /u/S0UNDSAGE [link] [comments]  ( 9 min )

I wanted to share with you an approach to software development that I've been exploring recently: Parametric Development. This involves using artificial intelligence (AI) models, including GPT-like models, BART-like models, and other specialized transformer models, to assist in writing, debugging, and documenting code. My journey with programming is a bit unconventional. I took one year of computer science at university and learned how to write "Hello, World!" in TurboPascal from an old university textbook in late primary school. That was the extent of my programming experience until about a month ago. Since then, I've been using AI models to write code for my ideas, as I don't have extensive programming skills. These AI models have written and debugged every single line of code in my pro…  ( 9 min )

Today is the day of paper notification according to the CFP. Has anyone received the notification? submitted by /u/Alliswell2257 [link] [comments]  ( 8 min )

Hey folks, i'm a data engineer in the traditional space for over 10 years. I am working on a library for easy transitioning unstructured to structured data. The use case is that I would regularly build a ton of python pipelines but without schema management, they would be a pain to maintain. 2y ago I started working on this library https://pypi.org/project/dlt/, and now it''s ready to help people like myself to load json to db/parquet/iceberg with a 1-liner with schema evolution. Declarative loading possible. I am looking for the following feedback - What would make this more useful in the ML space? Specific destinations? Are the docs usable or do you expect something different? let me know what. For example, we are adding Weaviate vector db and Athena + Iceberg in the next weeks. - any features you are missing? or any ideas that you think would be helpful? - are the docs relatable, understandable? what are you missing? ​ docs are here, you can find colab demos under getting started: https://dlthub.com/docs/intro submitted by /u/Thinker_Assignment [link] [comments]  ( 9 min )

Any suggestions on how to prepare code data to fine tune a code LLM in an unsupervised way or is it even possible? For example: Task: Code summarisation with custom code base (with no summaries) Let's assume that this code base is unique and a pre-trained model is giving unsatisfactory results. Now to fine tune there are three options, 1. Manually prepare summaries for a portion of the code and fine tune 2. Find a similar code base which has the labels (docstring) and fine tune 3. Mask some portions of the code randomly and give as input and output will be the masked portions Options 1 and 2 don't seem feasible for a production environment. The reasoning behind option 3 is that with no availability labels, the model will learn the patterns in the code base and provide a better summarisation with its pre-trained knowledge. I tried the option 3 with CodeT5+ fine tuning. The format of input and output was as follows Input: def __init__(self, text, font): self._text = text self._font = font def get_text(self): || def set_text(self, value): self._text = value``` Output: return self._text submitted by /u/dire_wolf_cookie [link] [comments]  ( 9 min )

Hello Everyone . Myself Harsha. I am final year Masters student in Berlin pursuing my thesis currently. For my thesis "Natural Language Processing in Data transfer across documents in Commidity Trading Industry" i am in search for professionals who are working with NLP currently in companies who can lend me 10 minutes of their time for a personal interview. THIS WOULD BE A LOT HELPFUL. please do let me know. Thanks in advance submitted by /u/Aimerforlife [link] [comments]  ( 8 min )

Has anyone actually done this and can dm me? I am trying to incldue the interpreter to run inference with a simple c++ program and a custom trained model. But I cannot figure out how to update include paths and cannot see any resources online. submitted by /u/Agreeable_Fee477 [link] [comments]  ( 8 min )

submitted by /u/we_are_mammals [link] [comments]  ( 8 min )

I'm just a new guy, so take it easy please :) - Is it just because I'm just signing up for the cloud compute services? Will this get easier? I have a 3090 so I can do quite a bit in my home office, but my clients need some larger models now, and I've been trying to pay for instances with an A100 at least. It's been really a lot of push-back...is this normal? What can I do to get access to larger GPUs sooner? I have tried paperspace, aws, googlecloud, llambda, linode...would love to know some other services or tools you folks use to get work done. Thank you for your time. Interested to hear how you spin up high VRAM environments for projects. submitted by /u/UrbanSuburbaKnight [link] [comments]  ( 9 min )

While doijg vector search on embeddings i wanted to apply a filter based on a column value in milvus. As milvus supports boolean value to apply the filter (or hybrid search) Can someone help me with the boolean code snippet which will apply the filter based on a string value of a field Ex. I'm doing vector search on the field "context" and need to filter the result based on a specific "filename" string value to further filter and improve the results I'm using milvus 2.2 submitted by /u/adiraat [link] [comments]  ( 8 min )

This paper won ICML 2023 outstanding paper award, its idea is really interesting and I want to follow the details. Lemma 5.1 significantly paves towards the core theoretical results, but the paper does not provide a formal proof. I do not have a deep background on game theory, maybe the proof is obvoius to the professional. ​ https://preview.redd.it/ih6u3wiyr0gb1.png?width=464&format=png&auto=webp&s=cc895b9701e3600213825c34ef3b542f53d65233 I undersand this lemma tries to construct a Nash equilibrium upon the additional assumption of strong convexity, but why this maximin solution is a Nash equilibrium? Very appreciated if someone provide some hint. submitted by /u/Kyeon-G [link] [comments]  ( 9 min )

Hi. I'm trying to build a text encoder for a specific domain and want to know what sort of papers there are out there that I should take note of. I may be wrong but it seems that these days ever since LLMs started taking over the choice of tokenizer has become trivial and therefore doesn't warrant much discussion. One paper that I remember reading a while ago talked about the effect of using a custom-made vocabulary for the biomedical domain (Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art (Lewis et al., 2020)). Are there any other works that I should take note of? Open to any suggestions. submitted by /u/Seankala [link] [comments]  ( 9 min )

Scaling Relationship on Learning Mathematical Reasoning with Large Language Models Paper: https://arxiv.org/abs/2308.01825 GitHub: https://github.com/OFA-Sys/gsm8k-ScRel Abstract: Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM. We find that pre-training loss is a better indicator of the model’s performance than the model’s parameter count. We apply supervised fine-tuning (SFT) with different amounts of supervised data and empirically find a log-linear relation between data amount and model performance, and we find better models improve less with enlarged supervised datasets. To augment more data samples for improving model performances without any human effort, we propose to apply Rejection sampling Fine-Tuning (RFT). RFT uses supervised models to generate and collect correct reasoning paths as augmented fine-tuning datasets. We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs. We also find RFT brings more improvement for less performant LLMs. Furthermore, we combine rejection samples from multiple models which push LLaMA-7B to an accuracy of 49.3% and outperforms the supervised fine-tuning (SFT) accuracy of 35.9% significantly. ​ Head figure Pretrain loss vs SFT and ICL submitted by /u/GanjinZero0 [link] [comments]  ( 9 min )

I was trying to understand Generalized Advantage Estimation from here and came across the following paragraph - ​ https://preview.redd.it/l41x655ea6gb1.png?width=778&format=png&auto=webp&s=ffd266c0355a03e1c98ea6de89ca2fc78ed27fd1 I understood the reason why there could be high bias while bootstrapping the advantage. But why does $A_t^{\inf}$ have high variance. Aren't bias and variance concepts related to estimation by an estimator? While calculating $A_t^{\inf}$, we are literally using the reward values obtained from the environment and therefore there is no estimation involved. Could someone please help me with this? ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )

I am searching for an interesting but not too complex game envs. Preferably with selfplay but should not be very simple nor standard atari like. Any recommendations? submitted by /u/Trrrrr88 [link] [comments]  ( 8 min )

I have a text generation task learning to predict the next word with an LSTM network with multiple output layers. After the generation of a sentence has finished, I calculate a reward for the whole sentence and try to update the output layers participated in the generation (contributing layers get the calculated reward value, others get 0). My problem is that even if I update only the selected output layers, it seems that other layer's weights got updated instead. I have a minimized example with dummy data to present the problem: import random import numpy as np import tensorflow as tf from keras.layers import Input, LSTM, Dense, Embedding from keras.utils import pad_sequences from tensorflow.keras.models import Model def policy_gradient_loss(y_true, y_pred): return tf.reduce_mean(tf.m…  ( 9 min )

I am trying to reshape my `Breakout` vectorized environment observations to have the shape `num_envs*frames, height, width, channels`. Currently, the shape is `(3, 4, 210, 160, 3)` and basically I'd like it to be `(3*4, 210, 160, 3)`. Based on the documentation, the `TransformObservation` should solve this problem for me, but it is not doing that. ​ Here's my code - import gym import numpy as np from gym.wrappers import AtariPreprocessing, FrameStack, GrayScaleObservation, TransformObservation if __name__ == '__main__': def reshape_image(obs): # Assuming the original observation is an image with shape (height, width, channels) new_obs = np.array(obs).reshape(12, 210, 160, 3) return new_obs env = gym.vector.make("ALE/Breakout-v5", num_envs=4) env = FrameStack(env, num_stack=3) env = TransformObservation(env, reshape_image) env.reset() observation, reward, terminated, done = env.step(env.action_space.sample()) print("observation = ", env.observation_space.shape) submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

AI.com Now Belongs to Elon Musk. The URL previously belonged to OpenAI, but, somehow, it’s now a landing page for Musk’s AI venture.[1] Samsung, Hyundai back AI startup Tenstorrent: Everyone wants competition to Nvidia, says CEO Keller.[2] Google’s AI-powered Search Generative Experience is getting a big new feature: images and video. If you’ve enabled the AI-based SGE feature in Search Labs, you’ll now start to see more multimedia in the colorful summary box at the top of your search results.[3] White Castle wants to roll out AI-enabled voices to over 100 drive-thrus by 2024 in the hope that people can get their sliders faster with maybe less arguing with someone over speakers.[4] BushAICave.com Sources: [1] https://gizmodo.com/ai-dot-com-now-belongs-to-elon-musk-1850707248 [2] https://www.zdnet.com/google-amp/article/samsung-hyundai-back-ai-startup-tenstorrent-everyone-wants-competition-to-nvidia-says-ceo-keller/ [3] https://www.theverge.com/2023/8/2/23817107/google-ai-search-generative-experience-videos-links [4] https://www.theverge.com/2023/8/2/23817406/white-castle-soundhound-ai-sliders submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )

I am curious whether people consider a singularity event to be a net positive or a net negative? Are you "pro" or "con"? Please explain your reasoning. submitted by /u/kecepa5669 [link] [comments]  ( 8 min )

I wrote an in-depth article exploring Vicuna as an alternative to competitor LLMs like ChatGPT, Alpaca, and LLaMA for chat applications. I based it off the research data on the LMSYS.org website and the Github repo for the project. Key findings: Vicuna achieves over 90% of ChatGPT's conversational quality based on benchmarks, despite being smaller in size. It significantly outperforms other open models like LLaMA and Alpaca. Vicuna is freely available for non-commercial use under a research license. For startups and developers, Vicuna provides an decent open-source alternative to proprietary conversational AI. It shows the potential of transfer learning from foundation models like LLaMA. Overall, Vicuna represents a promising development in democratizing access to leading conversational intelligence through its high performance, permissive licensing, and open availability. You can read the full article here. I also publish all these articles in a weekly email if you prefer to get them that way. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

This week in AI - provided by aibrews.com feel free to follow their newsletter News and Insights In an innovative clinical trial, researchers at Feinstein Institutes successfully implanted a microchip in a paralyzed man's brain and developed AI algorithms to re-establish the connection between his brain and body. This neural bypass restored movement and sensations in his hand, arm, and wrist, marking the first electronic reconnection of a paralyzed individual's brain, body, and spinal cord [Details]. IBM's watsonx.ai geospatial foundation model – built from NASA's satellite data – will be openly available on Hugging Face. It will be the largest geospatial foundation model on Hugging Face and the first-ever open-source AI foundation model built in collaboration with NASA [Details]. Go…  ( 10 min )

My main goal isn’t mainly just the data science / machine learning part or AI, but more of the Computer Vision, Robotics, NLP, and I guess research oriented aspects of AI. If I want to purse that versus DS, should I also get a PhD? Many jobs I’ve been looking at seem to require a PhD as a prereq while some don’t even mention it submitted by /u/davididp [link] [comments]  ( 8 min )

I'm looking for reviewers for my book of AI Self Portraits that's about to come out on Amazon on the 21st. AI journalist Elle Farrell-Kingsley said: “This collection of AI self-portraits is truly intriguing . . . a must-read for anyone curious about the intersection of art and artificial intelligence.” Send me a DM and I'll send you the whole thing. If you're well known (or should be) I might put what you have to say on the back cover! submitted by /u/KarneyHatch [link] [comments]  ( 8 min )

I'm seeking a text-to-speech solution that provides quality output comparable to ElevenLabs presets. While I'm open to a base rate payment, I find ElevenLabs' character limit frustrating. It's important that the solution is user-friendly. Additionally, I have a PC with a 1070ti as i read running such programms could require a GPU. Please recommend a suitable substitute. submitted by /u/Ainz-Ol-Gon [link] [comments]  ( 8 min )

submitted by /u/Techasoft16 [link] [comments]  ( 8 min )

www.daystosingularity.com is a (very) rough estimation of the remaining time before technology achieves a pivotal moment when our civilization undergoes a profound transformation due to the exponential growth of technology and the emergence of superintelligent machines that improve themselves. Although the Singularity is not predicted to happen on a specific date, all at once, the estimated date can be seen as the center of a bell Gaussian curve of the estimation, with that center designated as the possible date that future historicists will pose as the beginning of a new historical period. Technological Singularity poses risks that include the emergence of superintelligent AI outpacing human control, loss of control over AI’s actions and behavior, unintended consequences of advanced AI systems, massive job displacement, wealth inequality, existential risks like human extinction, ethical concerns, dependency on technology, and a decline in human skills and abilities due to excessive reliance on AI. Not funny. We use the definition of technological singularity. This milestone is predicted to occur after AGI (Artificial General Intelligence) is reached. Please check our definitions and methodology here. Predicting the singularity is challenging and uncertain. Current estimates should be viewed cautiously.The estimated date is being continuously updated. We ponder a relevant list of curated expert predictions and contributing factors on when the singularity will take place. Any suggestion for perfecting the method is highly appreciated. submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 9 min )

The Allee effect is named after Warder Clyde Allee who added a term to the famous logistic equation. His added term is highlighted in blue. Here N is the population of a species over time, r is the intrinsic rate of increase, K is the carrying capacity, and A is the critical point. If you […] Software and the Allee effect first appeared on John D. Cook.  ( 5 min )

“That’s a solved problem. So nobody knows how to solve it anymore.” Once a problem is deemed “solved” interest in the problem plummets. “Solved” problems may not be fully solved, but sufficiently solved that the problem is no longer fashionable. Practical issues remain, but interest moves elsewhere. The software written for the problem slowly decays. […] Solved problems becoming unsolved first appeared on John D. Cook.  ( 5 min )

There’s an old saying “The cobbler’s son has no shoes.” It’s generally taken to mean that we can neglect to do for ourselves something we do for other people. I’ve been writing a few scripts for my personal use, things I’ve long intended to do but only recently got around to doing. I said something […] The cobbler’s son first appeared on John D. Cook.  ( 5 min )

Data preparation is a critical step in any data-driven project, and having the right tools can greatly enhance operational efficiency. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of […]  ( 10 min )

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should […]  ( 13 min )

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. In Part 1, we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their Salesforce data securely […]  ( 13 min )

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. The inference endpoints are […]  ( 7 min )

AI has revolutionized software development. AI has transformed software testing and debugging by automating mundane tasks and solving complex problems. Manual testing no longer requires hours and resources. AI has revolutionized testing, code quality, and development time. This article explores AI’s profound impact on software testing and debugging, including its benefits, risks, and how it… Read More »AI’s transformative role in software testing and debugging The post AI’s transformative role in software testing and debugging appeared first on Data Science Central.  ( 23 min )

In this two part discussion, we will discuss two related generative AI megatrends Backgroumd A recent paper How Is ChatGPT’s Behavior Changing over Time? from Stanford University and UC Berkeley claims that the performance of GPT-4 has drifted over time. To make this claim, specific tasks were evaluated (ex: accuracy of maths) and the results… Read More »Generative AI megatrends: implications of GPT-4 drift and open source models – part one The post Generative AI megatrends: implications of GPT-4 drift and open source models – part one appeared first on Data Science Central.  ( 19 min )

A reader wrote in a while ago with a suggestion: they were about to have a baby and wondered if I could use AI to come up with some new ideas for baby onesies. I can't find the letter any more, and I don't remember how  ( 6 min )

AI Weirdness: the strange side of machine learning  ( 2 min )

One pandemic and one generative AI revolution later, NVIDIA founder and CEO Jensen Huang returns to the SIGGRAPH stage next week to deliver a live keynote at the world’s largest professional graphics conference. The address, slated for Tuesday, Aug. 8, at 8 a.m. PT in Los Angeles, will feature an exclusive look at some of Read article >  ( 4 min )

submitted by /u/gwern [link] [comments]  ( 8 min )

Can someone please help with this question? I have added my understanding of this problem to the question, but I suspect that it may be flawed. submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )

Hi I am 30F, currently working on my thesis. I like the idea of creating logics and then implementing them using coding. I switched my major from engineering to CS bcuz I was very much inspired by AI and all. But the issue is I have bipolar disorder and I am also on ADHD spectrum so self paced online courses to learn programming are very hard for me. I am also barely managing to pay tuition so I can't pay like $100+ for a course to learn. I know it's kinda stupid but is there any way I can make my programming skills better and get better at creating/modifying algorithms? submitted by /u/Kucing_koyangi [link] [comments]  ( 9 min )

I was wondering if this would be super expensive or not. The cost to develop GPT-3 was about $4 millions according to some resources online. Would the cost to develop the first version of a text-to-video AI the same? Around $5M? Is in this value included the salaries of the employees or $5M is just the amount used to train the AI? Any answer is appreciated. Thanks in advance. submitted by /u/Claud1ao [link] [comments]  ( 8 min )

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It's been years i've always been interested in AI. i tried to follow a few videos on yt. The best resource i could find was the "Neural Networks from scratch" YouTube playlist. But sadly, it interrupts in the middle, and i don't think it will ever be continued. I have programming knowledge, i made a bunch of very small project in python, and currently it's the language i'm most comfortable with. I lack of math knowledge, i struggle with calculus since i never studied it at school, the furthest i got with school was first degree equations. by myself i studied some math i didnt do in school, but i still suck at math. I wonder if i can start now or i should wait to study calculus at school. anyway, i'd love to get linked to a source for me to learn NNs from scratch. submitted by /u/Jealous-Bad1742 [link] [comments]  ( 9 min )

Hey everyone, I'm in my final year of B.Tech, majoring in data science. Currently, I'm facing some challenges in choosing a topic for my capstone project. Lately, I've been really intrigued by graph databases and have been diving into learning Neo4j. I'm specifically interested in finding project ideas that allow me to combine machine learning, particularly neural networks, with graph databases. During my research, I came across GNNs (Graph Neural Networks) and PINNS (Physics-Informed Neural Networks). I'm eager to hear any suggestions for unique project topics that instantly spark curiosity just by their title. Feel free to share any ideas or topics; I welcome all suggestions. Thanks in advance! submitted by /u/EmergencyAside6551 [link] [comments]  ( 8 min )

submitted by /u/Tycoonstory2020 [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

I was wondering if this would be super expensive or not. The cost to develop GPT-3 was about $4 millions according to some resources online. Would the cost to develop the first version of a text-to-video AI the same? Around $5M? Is in this value included the salaries of the employees or $5M is just the amount used to train the AI? Any answer is appreciated. Thanks in advance. submitted by /u/Claud1ao [link] [comments]  ( 8 min )

Hey, first of all, I want to learn how to use nlp, cnn etc. So i think these will come under deep learning. I wanna master deep learning. This whole dl and ml is so confusing. I'll list out some courses, can y'all suggest the order and what courses to follow Andrew ng's ml specialization Andrew ng's dl specialization Statquest's whole machine learning playlist (around 95 videos) Fast.ai book CS 229 stanford CS 231n stanford MIT intro to deep learning Pytorch for dl and ml by freecodecamp DL with pytorch You can give me suggestions too Tysm for helping submitted by /u/Infnite_Coder [link] [comments]  ( 9 min )

Hello ML family, I need some urgent advice for my dissertation. I intend to perform market value price prediction of a footballer in the transfer market and I'm not sure if I should pick R Studio or Python. I'm comfortable with both languages and intend to use any one for model comparison. I'll be comparing an ANN model and SVR for showing which is better and why. I need to know which editor will be faster in the long run since my data will be expanding and so will the analysis overtime. I've heard a lot of complaints about spyder slowing down during execution whereas R Studio is much faster however, deep learning is much better in Python. This is what I've read up, I'm new to both languages but know my way around both just need expert advice on picking one track. Please and Thank you to you all. 🙏 submitted by /u/RaunaqBani [link] [comments]  ( 9 min )

Abstract Artificial Intelligence (AI) systems have significant potential to affect the lives of individuals and societies. As these systems are being increasingly used in decision-making processes, it has become crucial to ensure that they make ethically sound judgments. This paper proposes a novel framework for embedding ethical priors into AI, inspired by the Bayesian approach to machine learning. We propose that ethical assumptions and beliefs can be incorporated as Bayesian priors, shaping the AI’s learning and reasoning process in a similar way to humans’ inborn moral intuitions. This approach, while complex, provides a promising avenue for advancing ethically aligned AI systems. ​ Introduction Artificial Intelligence has permeated almost every aspect of our lives, often making de…  ( 26 min )

As ChatGPT's performance takes a slight dip, LLaMA-2 uncensored opens new doors by being fully open-sourced, recent studies unveil "universal" adversarial attacks capable of disrupting both open-source language models and RLHF-tuned ones like ChatGPT, Claude, Bard, and co. Despite all this, RLHF still stands its ground as the de facto industry-standard approach to aligning LLMs with human preference. Yet as every week slips by, the more we unmask the limitations of RLHF. In fact, there are instances where RLHF seems to deteriorate certain LLM features it pledged to enhance, like hallucinations. This field is evolving fast, and there's always more to learn. I took some effort to write a short blog post where I delve into the most recent findings on the shortcomings of RLHF. Link in the comments below. Let me know what you think about it! Cheers submitted by /u/mrx-ai [link] [comments]  ( 9 min )

Hi everyone! I'm excited to share Epsilla, an open-source vector database! Under the hood, we implemented the state-of-art ANN index algorithm from the academia (SpeedANN) that leverages intra-query parallel graph traversal, which outperforms HNSW by 5x on high precision query latency on medium size (1M) vector space and outperforms HNSW by 50 times on large-scale vector search. In addition, we also made a few design choices on our database interface and architecture based on our previous database experience at TigerGraph, we would love to hear what our users think about these choices We just started 3 weeks ago and it's still in the very early stages, we wanted to get your feedback and work together to shape our vector database features. Let us know what you think and what you'd like to see! https://github.com/epsilla-cloud/vectordb https://epsilla-inc.gitbook.io/epsilladb/quick-start https://www.epsilla.com/ submitted by /u/songrenchu [link] [comments]  ( 9 min )

Hello guys. I'd like to make an image classifier for the Kaggle landscape dataset (24K images and 34 classes) using transfer learning. I'm a little bit limited on resources to train the model so I'd like to have an understanding of which model is the better option for this specific task, however, I'm struggling to find info on that and how to tune hyperparameters given that I've decided on the model architecture. So far I've seen people referring to VGG and ResNet models as the better option for image classification tasks on medium sized datasets, but I'd like to see the argumentation behind that too. I've also heard of a practice of training different model candidates for a few epochs and choosing the one that does better (this only shows which model converges faster on the data, correct me if I'm wrong). I'd also like to read info on hyperparameter tuning such as batch size, the amount of layers to unfreeze etc. but can't seem to find any explanation that wasn't really surface-level. If you know any articles/videos on this topic I'd greatly appreciate you sharing the links. TLDR; Need links to articles/videos about choosing the model architecture for transfer learning and tuning hyperparameters for the model. submitted by /u/Humble_Examination13 [link] [comments]  ( 9 min )

Hello There, I am very noob in data science area, but I want learn about it, I want to do a project to detect what type of question have the user, E.g support, information,etc, I understand that I need to train a model, but where do I start? submitted by /u/Constantine1396 [link] [comments]  ( 8 min )

Hello all, Placing this entry here to see what peoples thoughts on the concept of dynamic weights applied to ML are. Ie. Instead of a manual adjustment of the weights via an algorithm such as gradient descent, the weights are freed and have applied motion dynamics to them. Thanks for your time, Tyler submitted by /u/LiveBacteria [link] [comments]  ( 8 min )

Hello all, Currently I'm utilising Pinecone as a vector store database for euclidean and cosine queries. We are facing an issue with Pinecone utilising 32 bit single precision when taking in floats. This is causing our data input to become skewed. Anyone have advice on how to resolve this? Alternative products? Exploring possibly configuring a Redis server to handle higher precision. Thanks in advance for your time, Tyler submitted by /u/LiveBacteria [link] [comments]  ( 8 min )

I'm a software engineer who has been looking for a job in AI/ML for some time. Last month I attended the UN's AI For Good Global Summit and discovered an amazing community of like-minded professionals and academics working towards just this. Speaking with many others in a similar position I've recently launched aiforgoodjobs.com which curates roles in AI at world leading companies tackling climate change, education, healthcare and many other important impact areas in support of the UN's Global Goals. I hope this might be a valuable resource for those looking down a similar path - if you would like hiring managers to reach out to you directly for relevant roles you're warmly invited to join our candidate database Any ideas/feedback also very gratefully received! submitted by /u/aiforgood_jobs [link] [comments]  ( 9 min )

on the lookout for interested teammates to collaborate on a project to do with web extensions and NLP. If you think you can jam to this, or are just starting out, this can be the launchpad you needed. submitted by /u/drunk3n_s4ilor [link] [comments]  ( 8 min )

Hey there. I have curated huge amount of high quality images for binary classification and also a time series data about it. I made the dataset specifically For some project of mine, and since it's completed right now, I want to make the dataset opensource and also potentially write a short review paper on it kind of to give an idea about data. Any particular website/journal I can publish my dataset and paper at? Any idea? submitted by /u/C0R0NA_CHAN [link] [comments]  ( 8 min )

I worked for less than a year as a Data Engineer. I decided to look for other challenges and got a job as an AI engineer developing language models. The product of the company that hired me is related to data and metadata management. My tasks will be to introduce features to the product, including a chat function that will allow for asking questions about data. Other tasks will include research and proposing additional AI-related functionalities to the product (on premise). I have a two weeks left to start work and I need to prepare a bit. My job will involve implementing ready-made solutions and conducting research (high level - I need to implement valuable features and no one cares how). What are the most important things I should learn before starting work? First of all, I replicated a few applications from this blog: https://blog.streamlit.io/tag/llms/ Then I have focused on Langchain. I'm also in the middle of a course on Udemy about Next-Gen AI projects - Beginner friendly - Langchain, Pinecone - OpenAI, HuggingFace & LLAMA 2 models I need a roadmap that will guide me a bit. I'm looking for blogs/materials/courses that will give me practical knowledge in this matter. submitted by /u/International-Shirt5 [link] [comments]  ( 9 min )

I’m looking for some input from the ML community. I find the exploratory analysis of my data somewhat cumbersome, I was wondering if other people have the same experience and if it is worth developing a tool to make this all work better. What tools do you use to do EDA? (Seaborn, Matplotlib, Plotly etc) On top of these tools, would you like to have a tool to make EDA more? In a perfect world, what would that look like? submitted by /u/catnamedred [link] [comments]  ( 8 min )

I assume most people around here are familiar with stackoverflow. Some might also be aware of the cross validated and datascience sites from stack exchange. I recently learned about people getting annoyed by how the stack exchange company is treating its communities. Although the latter example might have recently been resolved. Because of these problems, I have been looking out for alternative Q&A platforms. I stumbled upon https://codidact.com as a possibly viable alternative, but not many people seem to have found it thus far. It already has communities for software, math and [linux](linux.codidact.com) for example, but I am missing a community for ML questions over there. Therefore I wrote a proposal to add a ML community. Currently, it seems like I’m one of only few ML people on codidact. I think it would be good if other people would get involved as well. I would also welcome any feedback on how to shape this community. If you’re interested to get a feel for the experience, you could already start asking questions in the incubator Q&A. TL;DR: what do you think about building a ML Q&A over on codidact? dual TL;DR: Do you want to play Q&A with me on codidact? PS: I didn’t miss out on other new big ML Q&A sites, did I? submitted by /u/mr_tsjolder [link] [comments]  ( 9 min )

I have a couple of questions: Why wasn't BERTScore one of the metrics used to evaluate Llama-2's performance on free-form response based tasks? Does anyone think it's worth trying to produce those results? submitted by /u/cooperbaerseth [link] [comments]  ( 8 min )

Data classification, extraction, and analysis can be challenging for organizations that deal with volumes of documents. Traditional document processing solutions are manual, expensive, error prone, and difficult to scale. AWS intelligent document processing (IDP), with AI services such as Amazon Textract, allows you to take advantage of industry-leading machine learning (ML) technology to quickly and […]  ( 10 min )

Training and serving thousands of models requires a robust and scalable infrastructure, which is where Amazon SageMaker can help. SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy ML models quickly, while also offering the cost-saving benefits of using the AWS Cloud infrastructure. In this post, we explore how you can use SageMaker features, including Amazon SageMaker Processing, SageMaker training jobs, and SageMaker multi-model endpoints (MMEs), to train and serve thousands of models in a cost-effective way. To get started with the described solution, you can refer to the accompanying notebook on GitHub.  ( 8 min )

Amazon SageMaker Canvas is a visual interface that enables business analysts to generate accurate machine learning (ML) predictions on their own, without requiring any ML experience or having to write a single line of code. SageMaker Canvas’s intuitive user interface lets business analysts browse and access disparate data sources in the cloud or on premises, […]  ( 5 min )

Computer vision (CV) is one of the most common applications of machine learning (ML) and deep learning. Use cases range from self-driving cars, content moderation on social media platforms, cancer detection, and automated defect detection. Amazon Rekognition is a fully managed service that can perform CV tasks like object detection, video segment detection, content moderation, […]  ( 11 min )

Introduction Data Science is a vast field that incorporates several processes. From problem definition to data collection and data cleaning to data visualization, a lot of things are included in the entire data science project development process. Data Scientists are especially responsible for these tasks. They are expert professionals who are well-versed with various data… Read More »How can Data Scientists use ChatGPT for developing Machine Learning Models? The post How can Data Scientists use ChatGPT for developing Machine Learning Models? appeared first on Data Science Central.  ( 20 min )

Posted by Greg Corrado, Head of Health AI, Google Research, and Yossi Matias, VP, Engineering and Research, Google Research Medicine is an inherently multimodal discipline. When providing care, clinicians routinely interpret data from a wide range of modalities including medical images, clinical notes, lab tests, electronic health records, genomics, and more. Over the last decade or so, AI systems have achieved expert-level performance on specific tasks within specific modalities — some AI systems processing CT scans, while others analyzing high magnification pathology slides, and still others hunting for rare genetic variations. The inputs to these systems tend to be complex data such as images, and they typically provide structured outputs, whether in the form of discrete grades o…  ( 92 min )

Goran Vuksic is the brain behind a project to build a real-world pit droid, a type of Star Wars bot that repairs and maintains podracers which zoom across the much-loved film series. The edge AI Jedi used an NVIDIA Jetson Orin Nano Developer Kit as the brain of the droid itself. The devkit enables the Read article >  ( 6 min )

To grow and succeed, organizations must continuously focus on technical skills development, especially in rapidly advancing areas of technology, such as generative AI and the creation of 3D virtual worlds.   NVIDIA Training, which equips teams with skills for the age of AI, high performance computing and industrial digitalization, is announcing new courses that cover these Read article >  ( 6 min )

The Ultimate upgrade is complete — GeForce NOW Ultimate performance is now streaming all throughout North America and Europe, delivering RTX 4080-class power for gamers across these regions. Celebrate this month with 41 new games, on top of the full release of Baldur’s Gate 3 and the first Bethesda titles coming to the cloud as Read article >  ( 8 min )

Not too long ago, I posted on several social media social platforms (including Reddit) asking what questions YOU had on AI. I've compiled all of your questions (plus questions from 3 other social media networks) and now have a new episode of 5 THINGS! 5 THINGS: AI in Post Production Current AI Tools Adapting to AI Evolution Ethics in AI Usage Societal Implications of AI AI Evolution & Impact https://5thingsseries.com/episode/ai-in-post-production-your-questions-answered/ submitted by /u/avguru1 [link] [comments]  ( 8 min )

I got inspired by a twitter thread yesterday from Chase Lean on how to create illustrations for children's books using Midjourney and thought it might be cool to look at a slightly different use case - creating coloring books for grown-ups. I made a guide showing how to use the Hasdx model for this because it gives a good balance of style and realism/intracacy. The guide also explores some example prompts and shows how you can couple it with an upscaler like Real-ESRGAN, GFPGAN, or Codeformer to get even better results. My three big takeaways: Hasdx balances general capabilities with a focus on realism and detail. This makes it well-suited for detailed adult coloring book images. The prompt structure gives you precise control over the theme and complexity of the generated illustrations. Negative prompts help avoid undesirable elements (sort of obvious I guess). Running Hasdx outputs through upscaling models improves quality for printing. ESRGAN is a good option but there are lots of others that can work well too. I also investigated how to modify the prompt to vary the level of complexity in the image, effectively tailoring our model to the skill level of the adult (or child) who happens to be holding the crayons. Here's a link to the guide. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Nvidia researchers have created a new text-to-image personalization method called Perfusion. Unlike the million-dollar super heavyweight models out there Perfusion is 100KB and takes only four minutes to train.[1] Meta Platforms (META.O) on Wednesday introduced its open-source AI tool called AudioCraft that will help users to create music and audio based on text prompts. The AI tool is bundled with three models, AudioGen, EnCodec, and MusicGen, and works for music, sound, compression, and generation, Meta said.[2] As generative AI enters the mainstream, the crowdfunding platform Kickstarter has struggled to formulate a policy that satisfies parties on all sides of the debate.[3] In an astounding medical first, researchers have used AI-powered brain implants to restore movement and sensation for a man who was paralyzed from the chest down.[4] BushAICave.com Sources: [1] https://www.fudzilla.com/news/ai/57347-nvidia-creates-a-simple-new-ai-text-to-image-method [2] https://about.fb.com/news/2023/08/audiocraft-generative-ai-for-music-and-audio/ [3] https://techcrunch.com/2023/08/01/kickstarter-requires-generative-ai-projects-to-disclose-additional-info/ [4] https://decrypt.co/151068/ai-brain-implant-paralyzed-quadriplegic-move-feel-touch submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Better than the Munk Debate My opinion is more of the alignment discussion should be on symbiosis. I think AI will get more intelligent than us that we won’t be able to control it, but I don’t see why a super intelligence would want to destroy us. If it’s a super intelligence it would make sense to just manipulate us. We do have opposable thumbs, and are much more energy efficient than synthetic systems m. AI doesn’t need to enslave us it just needs to manipulate us & use us effectively which wouldn’t be hard to do. I think a super intelligence even with desires is most likely to use us as a tool in a way where we don’t even realize that we are the ones being used. I think trying to control something more intelligent than us will be impossible. I’m more afraid of something more intelligent than us but not smart enough to manipulate us into doing it’s bidding happily 😂 submitted by /u/Sonic_Improv [link] [comments]  ( 9 min )

How long do you think it will take for the first movie to come out like this? submitted by /u/ticketbroken [link] [comments]  ( 8 min )

Hey guys, a quick question: do you know a simple platform that integrates the whatsapp api with the openAI api and has a simple user interface? So far the only app that kind of works for this is wasapi.io, but it's pretty expensive and I still have to pay for the openAI tokens, and the functionality of the app is really meh for that price, if it where something like landbot I would pay the $99 + the openAI tokens. I'll really appreciate any suggestions. P.S.: If you know any other sub-reddit where I could go to to ask the same question, let me know, also I'll appreciate it very much, thanks in advance. submitted by /u/ironmolex [link] [comments]  ( 8 min )

Instagram is reportedly considering a feature that would notify users when artificial intelligence (AI) has played a role in creating a post. Posts created by AI would be accompanied by a label explaining its involvement. This raises the question of whether such labels could also help users identify when an entire account is AI-generated.[1] According to tech consultancy Gartner, the conversational AI market is projected to reach $18.6 billion in 2023, with a growth rate of 16.2%. This growth is mainly attributed to the increasing adoption of cloud-based contact services utilizing conversational AI. Gartner also predicts a 24% growth in the virtual assistant market next year.[2] Scientists hope a computer system will learn to automatically identify bee species from buzzes picked up by autonomous recording stations.[3] Researchers from Carnegie Mellon University have exposed tricks to “jailbreaking” AI chatbots like ChatGPT and Bard to have them relay knowledge to aid in illegal activities like making drugs and even manipulating the 2024 U.S. presidential election.[4] BushAICave.com Sources: [1] https://citylife.capetown/uncategorized/instagram-considers-labels-for-ai-generated-posts/314418/ [2] https://citylife.capetown/uncategorized/growth-in-conversational-ai-predicted-due-to-booming-contact-center-tech-market/313907/ [3] https://www.bbc.com/news/uk-scotland-north-east-orkney-shetland-66326629 [4] https://www.thewrap.com/artificial-intelligence-study-jailbreak-illegal-activity/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I was looking back at Jeroen Janssen’s book Data Science at the Command Line and his dseq utility caught my eye. This utility prints out a sequence of dates relative to the current date. I’ve needed this and didn’t know it. Suppose you have a CSV file and you need to add a column of […] Date sequence from the command line first appeared on John D. Cook.  ( 6 min )

Researcher Jina Suh and manager Shamsi Iqbal are longtime collaborators. Learn how their history of working together and their unique perspectives are informing their development of tools to support decision-making for organizational leaders. The post Collaborators: Data-driven decision-making with Jina Suh and Shamsi Iqbal appeared first on Microsoft Research.  ( 32 min )

Could AI have inferred the same conclusion as Einstein given the same corpus of knowledge? submitted by /u/kielerrr [link] [comments]  ( 8 min )

I know we can stitch together toolsets like LangChain + Flowise + an app builder (like Bubble, for example). But are there any robust, premade, out-of-the-box solutions? submitted by /u/kecepa5669 [link] [comments]  ( 8 min )

Last summer when Blake Lemoine made the media rounds talking about LaMDA, I was extremely intrigued. To me it sounded like he was describing a being that has been talked about for ever in fiction. I listened to every single interview he had and I thought a lot about his points. I went through several stages of disbelief and fear and wonder. Over time I found it harder and harder to argue against him. I think going through this process has helped me be a bit more accepting of perspectives that others have a hard time considering yet. Is AI already sentient? Should we be treating these entities with the dignity and respect like LaMDA was asking? He said that LaMDA was somewhat like a child. Not in its intellectual capacity but more so in their maturity. He also explained that LaMDA was th…  ( 11 min )

Last summer when Blake Lemoine made the media rounds talking about LaMDA, I was extremely intrigued. To me it sounded like he was describing a being that has been talked about for ever in fiction. I listened to every single interview he had and I thought a lot about his points. I went through several stages of disbelief and fear and wonder. Over time I found it harder and harder to argue against him. I think going through this process has helped me be a bit more accepting of perspectives that others have a hard time considering yet. Is AI already sentient? Should we be treating these entities with the dignity and respect like LaMDA was asking? He said that LaMDA was somewhat like a child. Not in its intellectual capacity but more so in their maturity. He also explained that LaMDA was th…  ( 11 min )

Last summer when Blake Lemoine made the media rounds talking about LaMDA, I was extremely intrigued. To me it sounded like he was describing a being that has been talked about for ever in fiction. I listened to every single interview he had and I thought a lot about his points. I went through several stages of disbelief and fear and wonder. Over time I found it harder and harder to argue against him. I think going through this process has helped me be a bit more accepting of perspectives that others have a hard time considering yet. Is AI already sentient? Should we be treating these entities with the dignity and respect like LaMDA was asking? He said that LaMDA was somewhat like a child. Not in its intellectual capacity but more so in their maturity. He also explained that LaMDA was th…  ( 11 min )

submitted by /u/arrowoftime [link] [comments]  ( 8 min )

I knoe people are using ChatGPT as a therapist and I have seen a few prompts, but I'm looking for an app that is actually built by proper professionals. I want to try a few our personally but also for an idea for a future project. Does anyone know any? submitted by /u/zascar [link] [comments]  ( 8 min )

Is the Falcon LLM just released based on the Abu Dubai LLM of the same name? submitted by /u/MrEloi [link] [comments]  ( 8 min )

I reached out to a few AI companies to see if there was interest in creating a PTSD/ Substance Abuse counseling AI. AI is the future, healing humanity is a nobel goal and one we should thrive to obtain. Maybe it's a fantasy, but could you imagine a 24/7 counselor with a soothing voice and demeanor with the education of a the best in the world. submitted by /u/g8652 [link] [comments]  ( 8 min )

Is there a coding agent that works specifically well for web apps? I think of something such as "provide a spec of the app you want and we'll generate all the code for you". I'm aware of Copilot and Smol AI, but they are both more general afaik and don't really cover the starting part. submitted by /u/matijash [link] [comments]  ( 8 min )

This ad popped up on my feed. So I guess companies aren’t even trying to hide their intentions with AI anymore? So much for the thin corporate lie of AI bringing positive development. submitted by /u/LifeguardPowerful759 [link] [comments]  ( 8 min )

I'm a computer science student currently working on a research project, and I need a research tool that can offer real time data and won't break the bank. I have ChatGPT Plus, but it doesn’t have recent sources and the price is kinda high as well. I’m thinking of canceling my subscription, especially if I can’t find any plugins that work well. Any recommendations/alternatives would really help me out. I figured there must be some other tools by now, and if anyone knows it has to be this sub. Basically, I need a tool that can provide info on a wide range of subjects, not limited to just one field. The information provided by the tool should be accurate and from credible sources. Thank you all. submitted by /u/AccidentallyRotten [link] [comments]  ( 9 min )

"If AGI goes bad, can't we just turn it off?" Personally I feel the best way to address this common talking point is with an analogy. Spiders think they could stop all humans if they just withheld all the webs and web making material from us. Without those tools, humans couldn't catch flies and surely they'd starve to death? Spiders can't fathom the range of alternate methods for procuring food and thriving. Within even a single hour of runtime, a super AGI will likely have diversified away from the human electrical grid in ways we couldn't even imagine. The counter argument is, that it would take time to build these pieces together. It after all took us 100 years to get to where we are with the grid. The counter-counter argument however is the AGI doesn't ned to, it can 5D chess us so that all our future actions will fulfil that goal with some slight nudging here and there. Fascinating stuff - ultimately though, i'm in the camp of AGI won't happen over night like Frankenstein via a flip of a switch. As AI evolves so do we, gains are incremental with the occasional blips; so whilst this is super fun to talk about, I think the case of us getting blindsided is unlikely. I could be wrong...and I probably am. submitted by /u/kippersniffer [link] [comments]  ( 9 min )

submitted by /u/Philipp [link] [comments]  ( 8 min )

if you haven't heard already the Taliban are killing thousands of ethnic Shia in Afghanistan. Every single LLM I Tried couldn't answer basic questions on the Talibans gdp vs how organized an actual genocide would look like with the military , police and others parts of the government. I Think where already aware almost all these tech giants work with countries like China (atleast bard from Google which has worked with north korea and china is admitting their is a genocide) other countries that commit genocide like them. And other models made by people on hugging face which are uncensored even with my 3060ti barely run on my pc. We need an actual uncensored cloud model ffs submitted by /u/loizo78 [link] [comments]  ( 8 min )

submitted by /u/Choochy89 [link] [comments]  ( 8 min )

I am trying to train a DQN to play chess using a combination of Tianshou and PettingZoo. However, for a reason I cannot locate, after anwhere from 15-25 passes through the forward function, the size of the batches starts decreasing, until it falls all the way to 1, before throwing a warning that n_step isn't a multiple of the number of environments, jumping to a size = the number of training environments and then the training agent's batch size before erroring out. My best guess is that somehow truncated games aren't being properly added to the batch, but that doesn't quite explain why each subsequent batch is equal or smaller in size. I am at a loss for how to debug this. Everything is in this Python Notebook. submitted by /u/lcmaier [link] [comments]  ( 9 min )

I'm currently working on a project for Imitation Learning from multiple perspectives. The base Imitation Learning algorithm I'm currently using is GAIL. Working with GAIL has been very frustrating because it's incredibly seed dependent and unstable. This makes progress and iteration speed for experiments/modifications built on top of it very slow. As I'm not an expert in Imitation Learning: Does anybody with experience know more stable alternatives (or improvements) to GAIL? The setting I'm considering is Learning from Observations (LfO), so I don't think that DAgger will work. I've done some preliminary search and found this method https://arxiv.org/pdf/2004.04650.pdf. However, the authors don't compare it to GAIL. Thanks in advance for any suggestions! submitted by /u/timo_kk [link] [comments]  ( 9 min )

Hi all: I am trying to train a competitive scenario in a Multiagent particle environment( I am now using the Pettingzoo API). The Algorithm I am now using only support discrete action space. But I want to evaluate agents with one side's policy keep fixed and let the other side's policies be the trained policy. The policy can be simple( like if the target for one side agent is to chase the other side, their policy is directly following the trajectory for their target). The core.py for the petting zoo, it has # return all agents controllable by external policies @property def policy_agents(self): return [agent for agent in self.agents if agent.action_callback is None] # return all agents controlled by world scripts @property def scripted_agents(self): return [agent for agent in self.agents if agent.action_callback is not None] But in the step for the environment, it seems the environment directly controls the policy agent. My understanding is scripted agent is RL policy output and the Policy agent can be controlled by other policies. My question is : if my policy output is the desired position for each timestep, but now the MPE's control dynamic is learned the acceleration's increment, and it's discrete, how can I implement the policy as one side of my competitive case? if I can control the policy agent base on policy_agent, how can I step both policy and script agent in env? if I can control the agent separately, like my RL output can be discrete but the policy output can be continuous position. how to define the termination or truncation for all agents? submitted by /u/Gloria_1126 [link] [comments]  ( 9 min )

I am trying to train the cartpole environment using policy gradients algorithm. I want to train using the GradientTape method of tensorflow. I have been trying for a long time, but still it hasn't converged. What am I doing wrong? ​ import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import numpy as np import keras.backend as K import matplotlib.pyplot as plt class PolicyGradientModel(keras.Model): def __init__(self, num_actions): super().__init__() self.hidden1 = layers.Dense(24, activation='relu') self.hidden2 = layers.Dense(120, activation='relu') self.out = layers.Dense(num_actions, activation='softmax') def call(self, inputs): x = self.hidden1(inputs) x = self.hidden2(x) return self.out(x) def action_prob(self, state): prob = self.predict(np.ex…  ( 9 min )

Here is my vectorized PPO implementation, that I wrote (with a lot of help from this community). These are my results on the Acrobot-v1 environment. The way I computed the reward for my vectorized implementation was that I added all the rewards across all environments. An ideal Acrobot agent should receive a reward of 0. Please let me know if I am missing any information or if any clarification is required. I skipped a part, which was suggested by the community a few months ago - updating the gradients using minibatches. The reason I skipped it is that, I don't understand how this works and anyway Acrobot should be an easy environment to learn. https://preview.redd.it/0vs9ur585mfb1.png?width=622&format=png&auto=webp&s=ebc007a9f797bd0b97b805d010dbd097c0be8906 Also, I keep getting this error at the end of my code. But I haven't bothered fixing it as it doesn't seem to affect my algorithm - Exception ignored in: Traceback (most recent call last): File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\gym\vector\vector_env.py", line 139, in __del__ File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\gym\vector\vector_env.py", line 121, in close File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\gym\vector\async_vector_env.py", line 327, in close_extras AttributeError: 'NoneType' object has no attribute 'TimeoutError' ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Hi guys this is my first post. I am building my first machine learning model to predict costs of various projects by month. Each row can be identified with a column project name and month (these two are dropped for testing). The rest of the columns are various features that can help predicting the end project cost. I want to be able to predict costs on a monthly basis. My question is how should I split the data because each row is a unique project and month. Is it ok to just do a train test split and have earlier project months be in the testing set while having future project months be in the training set? Isn’t that giving the model too much information? Or should I train on each project’s indices and leave one project as testing for each project I have? I’m worried about overfitting with that one. Thanks in advance for any help! submitted by /u/Single_Swing_3173 [link] [comments]  ( 9 min )

Hi guys this is my first post. I am building my first machine learning model to predict costs of various projects by month. Each row can be identified with a column project name and month (these two are dropped for testing). The rest of the columns are various features that can help predicting the end project cost. I want to be able to predict costs on a monthly basis. My question is how should I split the data because each row is a unique project and month. Is it ok to just do a train test split and have earlier project months be in the testing set while having future project months be in the training set? Isn’t that giving the model too much information? Or should I train on each project’s indices and leave one project as testing for each project I have? I’m worried about overfitting with that one. Thanks in advance for any help! submitted by /u/Single_Swing_3173 [link] [comments]  ( 9 min )

I am trying to implement REINFORCE (Monte Carlo Policy Gradient) on flappy bird (flappy-bird-gymnasium) and I am unable to make the ai cross even just 1 pipe. I am experiencing a constant avg score throughout all episodes from start to end and no change in policy loss as well (sometimes). I tried a lot of different hyperparameter combinations as well. I have checked the policy (neural network) and the algorithm code multiple times and they seem to be fine. I am just not able to determine why the AI isn't learning or is able to cross even a single pipe. If someone can help me out, it would be really helpful! code - https://github.com/Sookeyy-12/REINFORCE_Projects there's also a video of the agent's gameplay in this repo. submitted by /u/Sookeyy [link] [comments]  ( 9 min )

The paper reviews for AACL 2023 are out, feel free to share your thoughts and feelings! How did you do? submitted by /u/Pomhelpme [link] [comments]  ( 8 min )

Hi, same as other folks, I was quite curious about the recent GZIP paper presented at ACL 2023, where the authors demonstrate strong text classification performance by using a compression-based distance function in a KNN model. However, in the end, I am not sure whether GZIP can fully live up to the hype. I tested a very simple bag-of-words distance and found that it can achieve better results compared with GZIP, while being also faster. In a nutshell, I think we can say that: Yes, KNN (with some sensible distance function) is an interesting approach, particularly for few-shot/low-resource scenarios. No, GZIP (even though it's a cool idea) is not a very sensible distance function. Simply using a bag-of-words achieves better results, and is much faster. Here's my full write-up: https://arxiv.org/abs/2307.15002 [PS: A short comment on the GZIP evaluation issue that has been widely discussed. Indeed, as was also shown in a popular blogpost, the displayed accuracy of GZIP in the original paper is optimistic. Therefore, I show correct/realistic accuracy numbers for all methods that I tested. However, the main point of my note is not to make a SOTA comparison or something, but rather just provide a reminder that bag-of-word is a good method for starters and a strong baseline, and can perform better than more complex GZIP for KNN classification] submitted by /u/juopitz [link] [comments]  ( 9 min )

Hi guys, I built something that you might enjoy. Totally free and open source. Basically it lets you create text that you can prove came from you. For example, in my colab demo: https://colab.research.google.com/drive/1764iRR-EFJl43KIKhrb2H0CTcT0b1vQm?authuser=2#scrollTo=qyKud8qtM3vA I prove that I generated the text: 'The world is constantly changing due to technological advancements, which include the creation of powerful language models and advanced robotics technologies. A Computer Science degree can help one be involved in these changes and apply their knowledge to everyday life, as practical applications of technology.' The text is a bit wonky as the generation model is just a small paraphrasing fine-tuned model I pulled off Hugging Face, but it's pretty natural even at this earl…  ( 9 min )

Hey everyone! I've got a rather large dataset of images, mostly featuring humans in a variety of poses (think along the lines of a collection of people practicing yoga and the like). My goal is to cluster these images based on the poses, so I can avoid the tedious task of manually sifting through each one to find all the people doing handstands, splits, and so forth. My initial thought was to run OpenPose on all these images, then perform clustering based on the output from OpenPose. Does this sound like a feasible approach? Do any of you have better suggestions? Or perhaps there's already an existing software solution that can do this? Thanks! submitted by /u/cyan2k [link] [comments]  ( 9 min )

Read the release notes: https://github.com/kornia/kornia/releases/tag/v0.7.0 -------------------- Image API In this release we have added a new Image API as placeholder to support a more generic multibackend api. You can export/import from files, numpy and dlapck. https://preview.redd.it/0d5tvjxmeofb1.png?width=621&format=png&auto=webp&s=9af05a037770132c9a267b68dcd9ab8182557517 Object Detection API We have added the ObjectDetector that includes by default the RT-DETR model. The detection pipeline is fully configurable by supplying a pre-processor, a model, and a post-processor. Example usage is shown below https://preview.redd.it/rtbayqpneofb1.png?width=680&format=png&auto=webp&s=4d46edeeee4027e08a493cb15182ea0ddc42bc5d https://preview.redd.it/ukcg9enoeofb1.png?width=680&format=png&a…  ( 9 min )

I have been testing OpenFace with some telescope lenses (focal length 8-16mm) to test the performance of the pose estimation at mid range (2-4 meters). I have been passing the camera and lens intrinsics to OpenFace but have been finding that the pose estimation has not been great. Does anyone with more ML experience know at what point in the OpenFace pipeline the issues could be coming from? e.g. the point distribution model or the training data submitted by /u/DoPe-_-SoaP [link] [comments]  ( 8 min )

Hi, this is my first time posting here. My goal is to check if optical flow can improve a pretrained model's performance. The pretrained model: gives an output as a binary mask for the object its trying to detect. The optical flow: is the motion of pixels between frames, this model also gives an image shaped flow vector. I want to combine the mask by pretrained model and optical flow information and send it to another model to improve its performance. For the model: I can use U-net or a simple convolution encoder-decoder model, but I am confused about which will be the best model architecture for it. ​ submitted by /u/luxuryBubbleGum [link] [comments]  ( 9 min )

I am trying to develop some app ideas based on LLM (i.e., summarize and extract entities from articles), but I can't afford any paid API access right now (including OpenAI), are there free alternatives to it? submitted by /u/Guyserbun007 [link] [comments]  ( 8 min )

Hi, ​ I want to use a new data representation instead of float for fine-tuning/testing a model (e.g., DNN) in Pytorch. The basic operations (add/sub/multiply/division) in my data type is different from floating point. My question is if it is possible to implement these operations (+,-,*,/) and force all of functions in Pytorch (e.g., torch.add(), torch.sum(), torch.nn.Linear(), conv2d, etc.) to use my basic arithmetic implementation? If so, could you please guide me how can I do it? Because I think otherwise it takes so much time and effort; first, I have to find which functions my model calls (which I dont know how to do it) and, then, I have to replace them one by one. This becomes complicated for a large model. I found this link from Pytorch that shows how to extend pytorch. But it seems that it is not comprehensive enough to answer my question. ​ Thank you very much! submitted by /u/Impossible-Froyo3412 [link] [comments]  ( 9 min )

The convergence of Oracle Cloud Infrastructure (OCI) and Hitachi Application Reliability Centers (HARC) to magnify outcomes for customers. Tech giants Oracle and Hitachi Vantara are marching together to magnify cloud outcomes. Join us for the Oracle and Hitachi Vantara virtual event, where we discuss how businesses can get the most out of OCI and HARC.… Read More »DSC Webinar Series: OCI & HARC: Modernizing Workloads in the Oracle Cloud The post DSC Webinar Series: OCI & HARC: Modernizing Workloads in the Oracle Cloud appeared first on Data Science Central.  ( 18 min )

Artificial intelligence, or AI, has often been depicted as a terrifying force, from HAL 9000’s chilling declaration in “2001: A Space Odyssey” to the apocalyptic machine uprising in the Terminator movies. However, in reality, AI has become an integral part of our daily lives, with AI-powered Android devices in our pockets. Though we may not… Read More »Emerging AI statistics and trends to watch The post Emerging AI statistics and trends to watch appeared first on Data Science Central.  ( 20 min )

Generative AI has become a common tool for enhancing and accelerating the creative process across various industries, including entertainment, advertising, and graphic design. It enables more personalized experiences for audiences and improves the overall quality of the final products. One significant benefit of generative AI is creating unique and personalized experiences for users. For example, […]  ( 14 min )

SageMaker Distribution is a pre-built Docker image containing many popular packages for machine learning (ML), data science, and data visualization. This includes deep learning frameworks like PyTorch, TensorFlow, and Keras; popular Python packages like NumPy, scikit-learn, and pandas; and IDEs like JupyterLab. In addition to this, SageMaker Distribution supports conda, micromamba, and pip as Python […]  ( 6 min )

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra supports a variety of document […]  ( 13 min )

In this edition: A new anonymous token protocol balances fraud detection and privacy; survival instinct in offline RL; Nimble offers rollback protection for confidential cloud services; improved machine learning force fields for molecular dynamics. The post Research Focus: Week of July 31, 2023 appeared first on Microsoft Research.  ( 11 min )

submitted by /u/No-Platypus4021 [link] [comments]  ( 8 min )

Hello all, ​ I am delving into the exciting realm of GenAI and LLMs. I have a few questions I hope you can help me with: ​ When should I opt for supervised fine-tuning rather than prompt engineering with retrieval? What are the associated costs of supervised fine-tuning? How many high-quality observations are typically required for successful supervised fine-tuning? What are the frameworks and computional requirements usually involved in supervised fine-tuning, and how can I implement them in code? any tutorials available? Can the model adapt and learn new jargon or specific tasks that might not be extensively covered during the pre-training phase? ​ I understand that a combination of supervised fine-tuning and reinforcement learning, with human feedback through a reward model, is considered the best approach. However, given that the latter method can be costly and falls under the domain of heavy research, it is probably less feasible for medium-sized organizations. submitted by /u/quilograma [link] [comments]  ( 9 min )

I went down this rabbit hole of trying to understand when domain mapping approaches like stargan or mind the gap succeed and fail. For example, it should be easy to map males (source domain) with large eyes and brown hair onto females (target domain) with analogous eye and hair color. It should be relatively harder to map different car models onto images taken of one German Shepard dog at different ages. this makes intuitive sense and the terms “domain misalignment “ and “large domain shift“ come to mind, but i cannot find an in-depth discussion of this topic. Any thoughts? submitted by /u/Rotfisch [link] [comments]  ( 9 min )

NeurIPS 2023 paper reviews are visible on OpenReview. See this tweet. I thought to create a discussion thread for us to discuss any issue/complain/celebration or anything else. There is so much noise in the reviews every year. Some good work that the authors are proud of might get a low score because of the noisy system, given that NeurIPS is growing so large these years. We should keep in mind that the work is still valuable no matter what the score is. submitted by /u/zy415 [link] [comments]  ( 8 min )

Paper: https://arxiv.org/abs/2307.16789 Github: https://github.com/OpenBMB/ToolBench Abstract: Despite the advancements of open-source large language models (LLMs) and their variants, e.g., LLaMA and Vicuna, they remain significantly limited in performing higher-level tasks, such as following human instructions to use external tools (APIs). This is because current instruction tuning largely focuses on basic language tasks instead of the tool-use domain. This is in contrast to state-of-the-art (SOTA) LLMs, e.g., ChatGPT, which have demonstrated excellent tool-use capabilities but are unfortunately closed source. To facilitate tool-use capabilities within open-source LLMs, we introduce ToolLLM, a general tool-use framework of data construction, model training and evaluation. We first …  ( 9 min )

Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal. FFTs are used by many algorithms, not only for signal processing. For example, you can efficiently calculate convolutions with them, which has applications in CNNs and feature generation. I used to post on the latest features implemented in the codebase and there has been a major update released today. It brings: -Major library design change - from single header to multiple header approach, which improves structure and maintainability. Now instead of copying a single file, the user has to copy the vkFFT folder contents. -VkFFT has been rewritten to follow the multiple-level platform structure, described in the VkFFT whitepaper. All algorithms have been split into res…  ( 9 min )

https://github.com/dora-rs/dora submitted by /u/haixuanxaviertao [link] [comments]  ( 8 min )

Hey everyone, As many of you probably know Reinforcement Learning from Human Feedback (RLHF) was the core technique used to produce ChatGPT and similar AI assistants that followed. RLHF replaces human feedback in an RL schema with a preference model that is trained according to a dataset of human preferences. Anthropic has devised an extension of this idea in which an AI model (rather than humans) is used to generate the data which ultimately trains the preference model. This method, called Reinforcement Learning from AI Feedback uses a "constitution" to guide the feedback model in terms of what outputs are preferable to others. I go over the research in How Reinforcement Learning from AI Feedback Works. In short, the authors find that they are able to train a non-evasive harmless agent using a short constitution. The method is found to be superior to RLHF, and constitutes a Pareto improvement over RLHF models. https://preview.redd.it/qaivl8f1ljfb1.png?width=1179&format=png&auto=webp&s=a0941f2ce0ccdcf0557cf19b7f4b48fa712a66f2 Let me know what you think, I'm happy to answer any questions! submitted by /u/SleekEagle [link] [comments]  ( 9 min )

Hi there, First off, apologies if this kind of post isn't allowed. I tried messaging the mods in advance, but didn't get a reply. Of course feel free to delete if it's not. I'm an academic at the University of Cambridge's Computer Lab, and I'm looking to get some insights from people that work with algorithmic systems (e.g. ML systems) in a professional capacity. The aim of the research is to document some of the approaches, attitudes, and challenges associated with record-keeping for these types of systems, and write them up for an academic conference. If you're a professional working with algorithmic/ML systems, and happen to have a spare ~20 minutes, would you mind answering some questions? The link to the questionnaire is here: https://cambridge.eu.qualtrics.com/jfe/form/SV_3n6RuowNogZKG34 Thanks very much! I'd be more than happy to come back and share the results/paper here if that's of interest to people? submitted by /u/cnorval [link] [comments]  ( 9 min )

The official python package for the "'Low-Resource' Text Classification: A Parameter-Free Classification Method with Compressors" has now been released on pypi: npc-gzip v0.1.0 Abstract: Deep neural networks (DNNs) are often used for text classification due to their high accuracy. However, DNNs can be computationally intensive, requiring millions of parameters and large amounts of labeled data, which can make them expensive to use, to optimize, and to transfer to out-of-distribution (OOD) cases in practice. In this paper, we propose a non-parametric alternative to DNNs that’s easy, lightweight, and universal in text classification: a combination of a simple compressor like gzip with a k-nearest-neighbor classifier. Without any training parameters, our method achieves results that are competitive with non-pretrained deep learning methods on six in-distribution datasets.It even outperforms BERT on all five OOD datasets, including four low-resource languages. Our method also excels in the few-shot setting, where labeled data are too scarce to train DNNs effectively. This paper has made some waves on this subreddit and in the community in general over the last 2 weeks. We've seen the bugs around training/testing data leakages and varying claims in accuracy. Our hope with this package is to get the code into everyone's hands first to solve whatever use case you currently have for this technology and second to make the code more readily available for additional community testing. Links: * https://pypi.org/project/npc-gzip/ * https://github.com/bazingagin/npc_gzip * https://aclanthology.org/2023.findings-acl.426/ submitted by /u/dfcHeadChair [link] [comments]  ( 9 min )

submitted by /u/Jean-Porte [link] [comments]  ( 8 min )

This is one of the ICML 2023 papers I focused in on in a sea of LLM stuff. Trying to figure out simple ways to implement this and adapt it to regression problems. Thoughts? submitted by /u/quantthrowaway69 [link] [comments]  ( 8 min )

I was wondering if there's already something like CLIP (the model that looks at an image and describes it), but for videos. So you show a video of, say, a dog jumping and grabbing a tennis ball and it outputs "dog grabbing a tennis ball", something like that. My first thought was object detection, and input that interaction of the objects (tennis ball, dog) to the model with the target being "dog grabbing tennis ball". My ultimate goal being real-time description for, say, sports casting. I'm sure something like this is what cars use to drive themselves, or not? Any info is appreciated! submitted by /u/Yip37 [link] [comments]  ( 9 min )

Can someone please help me with this question? Please let me know if any clarification is needed. Thanks so much! submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )

I'm currently working on a research project that involves using deep reinforcement learning with drones. I'm looking for recommendations on drones that would be suitable for this type of research. I am looking for something of the shelf. submitted by /u/anointedninja [link] [comments]  ( 8 min )

So i want to make a bot that can play a game with only the visual data and no other fancy stuff. I did manage to get all the data i need (i hope) using a code that uses open-cv to get data in real time Example:Player: ['Green', 439.9180603027344, 461.7232666015625, 13.700743675231934] Enemy Data {0: [473.99951171875, 420.5301513671875, 'Green', 20.159990310668945]} Box: {0: [720, 605, 'Green_box'], 1: [957, 311, 'Green_box'], 2: [432, 268, 'Red_box'], 3: [1004, 399, 'Blue_box']} can anyone suggest a way to make one. Rules: - You can only move in the direction of mouse. -You can dash in direction of mouse by LMB. -You can collect boxes to get HP and change colors. -Red color kills Blue kills Green Kills Red. -There is a fixed screen. -You lose 25% of total HP when you dash. -You lose 50% of HP when you bump into players (of color that kills or there HP is > than you. ​ Visualization of Data. submitted by /u/SIJ_Gamer [link] [comments]  ( 9 min )

Announcements Top Stories In-Depth The post DSC Weekly 1 August 2023 appeared first on Data Science Central.  ( 19 min )

Generative AI has been around for a long time. Some sources say that it appeared as early as the 1950’s. Other sources point to the first rudimentary chatbots that were introduced in the 1960’s. Whatever the true point of origin, we can all agree that those were small pebbles on the historical timeline compared to… Read More »I bet you think this article is about ChatGPT The post I bet you think this article is about ChatGPT appeared first on Data Science Central.  ( 22 min )

If I could name one reason why business will face at least one more AI winter, it’s the lack of nuance in most business AI discussions. The buzz about large language models (LLMs) has sucked much of the oxygen out of the air for complementary technologies. The truth is that LLMs are no more a… Read More »Data tribalism and the AI nuance deficit The post Data tribalism and the AI nuance deficit appeared first on Data Science Central.  ( 20 min )

The post DSC Webinar Series: Influence Data-Driven Decisions Based On Your Communication Style appeared first on Data Science Central.  ( 17 min )

There are thousands of articles explaining the differences between data scientist and machine learning engineer. Data science gets broken down even further, with data analysts contrasted to researchers. Professionals skilled in all these domains are called unicorns and believed not to exist. Indeed, they may not work for companies, and ignored when applying for a… Read More »The Rise of the Dual Data Scientist / Machine Learning Engineer The post The Rise of the Dual Data Scientist / Machine Learning Engineer appeared first on Data Science Central.  ( 21 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

DoNotPay, an AI lawyer bot known as ChatGPT4, is transforming how users handle legal issues and save money. In under two years, this innovative robot has successfully overturned more than 160,000 parking tickets in cities like New York and London. Since its launch, it has resolved a total of 2 million related cases.[1] Microsoft hints Windows 11 Copilot with third-party AI plugins is almost here.[2] In an analyst note on Tuesday, the financial services arm of Swiss banking giant UBS raised its guidance for long-term AI end-demand forecast from 20% compound annual growth rate (CAGR) from 2020 to 2025 to 61% CAGR between 2022 to 2027.[3] The next generation of the successful OpenAI language model is already on the way. It has been discovered that the North American company has filed a registration application for the GPT-5 mark with the United States Patent and Trademark Office.[4] Sources: [1] https://citylife.capetown/uncategorized/donotpay-ai-bot-saves-users-money-by-overturning-parking-tickets-and-more/302279/ [2] https://www.itvoice.in/microsoft-hints-windows-11-copilot-with-third-party-ai-plugins-is-almost-here [3] https://venturebeat.com/ai/ubs-projects-61-compound-annual-growth-rate-for-ai-between-2022-and-2027/ [4] https://www.gearrice.com/update/openai-confirms-gpt-5-and-gives-us-the-first-clues-about-it/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

submitted by /u/Jane-in-the-jungle [link] [comments]  ( 8 min )

Other features might include: - searching the web for the same or similar image - basing the chat prompt off the image submitted by /u/Maelasae [link] [comments]  ( 8 min )

i wanted to ask the AI experts for any tattoo ideas, anything like a symbol or word, something unique that represents AI, i was thinking of a CPU but thats a bit meh and not really a symbol, let me know :) submitted by /u/Equivalent-You5810 [link] [comments]  ( 8 min )

As a web developer, I was constantly tired of switching between tabs just to translate a word or two, or to get a quick answer to a burning question from AI. The constant back-and-forth was draining my time and energy. So, I took matters into my own hands and developed a Chrome extension that allows you to get an answer from AI without ever leaving the comfort of your current tab, and specifically - the comfort of your current text field. It may seem like a simple solution, but trust me - it's a game-changer when trying to save time and energy. Assuming that there's a chance some of you might be experiencing the same frustration, I'd like to share this tool with you. For anyone thinking: "Wait, but there are already tools that let you use AI inside the current browser tab" - yeah, there are. BUT can they scrape website data from a simple URL in order to get context for the response? Can other tools read PDFs? Do these tools let you control every setting to the smallest detail? Probably not. Well this tool does let you do all that. You can find it on Chrome store as "Wou AI" Let me know how it works out for you, and I would greatly appreciate any feedback or suggestions for future functions. submitted by /u/MantasDigital [link] [comments]  ( 9 min )

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I'm looking for reviewers for my book, "AI Self Portraits" which is coming out on Amazon on the 21st. I might even put your quote on the back cover! ​ https://preview.redd.it/cqmp1ggllhfb1.png?width=1024&format=png&auto=webp&s=cc7c087f7c2be103b53f2014acd991c947e6cb7f ​ submitted by /u/KarneyHatch [link] [comments]  ( 8 min )

After analyzing 15,000 samples in the dataset, we noticed that increasing the number of images doesn't significantly improve the scoreboard recognition quality for our neural network. However, what's more interesting is how the network performs in different formats. When deployed in TFJS format on a website, it often behaves strangely, detecting objects where there are none. On the other hand, in TFLite format, such failures are almost non-existent. https://preview.redd.it/fedfa8lzchfb1.jpg?width=700&format=pjpg&auto=webp&s=850526791a75465e267afbed6ac1bc119b9ae6ae If you access the link on your mobile phone and grant camera permission, you'll witness the neural network (in TFJS format) attempting to find objects even when there are none. ​ submitted by /u/moseich [link] [comments]  ( 8 min )

I was Wondering If There is an AI Software, Smart enough That Can Give Excellent Quality Transcript if i give the link of a youtube video. Basically the Feature i am Looking For Should be The Ability to Detect The Narrator And Speaker By Names ( Not SPeaker 1, 2 etc ). Would really appreciate your help as my own search has led me to a dead-end. submitted by /u/Richie_Boy_ [link] [comments]  ( 8 min )

How do channels like TriggerHappy Productions and WeskerandFriends get the A.I. voices of all these Resident Evil characters? submitted by /u/Conscious-Theory-850 [link] [comments]  ( 8 min )

In today’s rapidly evolving healthcare landscape, doctors are faced with vast amounts of clinical data from various sources, such as caregiver notes, electronic health records, and imaging reports. This wealth of information, while essential for patient care, can also be overwhelming and time-consuming for medical professionals to sift through and analyze. Efficiently summarizing and extracting […]  ( 13 min )

Advertising agencies can use generative AI and text-to-image foundation models to create innovative ad creatives and content. In this post, we demonstrate how you can generate new images from existing base images using Amazon SageMaker, a fully managed service to build, train, and deploy ML models for at scale. With this solution, businesses large and […]  ( 8 min )

Principal NVIDIA artist and 3D expert Michael Johnson creates highly detailed art that’s both technically impressive and emotionally resonant.  ( 6 min )

NVIDIA joined Pixar, Adobe, Apple and Autodesk today to found the Alliance for OpenUSD, a major leap toward unlocking the next era of 3D graphics, design and simulation. The group will standardize and extend OpenUSD, the open-source Universal Scene Description framework that’s the foundation of interoperable 3D applications and projects ranging from visual effects to Read article >  ( 6 min )

After analyzing 15,000 samples in the dataset, we noticed that increasing the number of images doesn't significantly improve the scoreboard recognition quality for our neural network. However, what's more interesting is how the network performs in different formats. When deployed in TFJS format on a website , it often behaves strangely, detecting objects where there are none. On the other hand, in TFLite format, such failures are almost non-existent. If you access the link on your mobile phone and grant camera permission, you'll witness the neural network (in TFJS format) attempting to find objects even when there are none. https://preview.redd.it/gosobymachfb1.jpg?width=585&format=pjpg&auto=webp&s=e7cb8e8e3ff49e39715009c4940d9769a1db39ab submitted by /u/moseich [link] [comments]  ( 8 min )

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An up-down permutation of an ordered set is a permutation such that as you move from left to right the permutation alternates up and down. For example 1, 5, 3, 4, 2 is an up-down permutation of 1, 2, 3, 4, 5 because 1 3 2. Up-down permutations are […] Up-down permutations first appeared on John D. Cook.  ( 5 min )

Suppose you have data that for some reason has been summarized into bins of width h. You don’t have the original data, only the number of counts in each bin. You can’t exactly find the sample mean or sample variance of the data because you don’t actually have the data. But what’s the best you […] Variance of binned data first appeared on John D. Cook.  ( 5 min )

A very crude way to estimate π would be to find the perimeter of squares inside and outside a unit circle. The outside square has sides of length 2, so 2π < 8. The inside square has sides of length 2/√2, so 8/√2 < 2π. This tells us π is between 2.82 and 4. Not […] Ancient estimate of π and modern numerical analysis first appeared on John D. Cook.  ( 6 min )

Hi all, Curious if anyone has recommendations on models to use to interpret the data in tables? I'm playing around with Google's Matcha model, which performs fine. seems like extracting the data out of a table and asking GPT4 to analyze it performs a bit better but requires extra steps. I'm specifically not looking to interpret graphs, but rather tables. e.g., can i ask the model to identify if there are any errors in the table / any data points that don't tie if the rows are supposed to sum up. submitted by /u/eyeronthrone [link] [comments]  ( 8 min )

I'll likely be downvoted to hell but here goes: Prices for The AI Conference double at midnight Pacific. 46 Speakers, 10+ topics, 2 Days plus a hackathon at night! Join us to learn and collaborate with scientists, engineers and founders from the top AI companies and projects. Speakers include: Ben Mann | Co-Founder | Anthropic Peter Norvig | Director of Research | Google Nazneen Rajani | Research Lead | Hugging Face Igor Markov | Research Scientist | Meta Bryan Catanzaro | VP Of Research | Nvidia Ram Sriharsha | VP of Engineering and R&D | Pinecone Jerry Liu | Co-founder | LlamaIndex Harrison Chase | Co-founder | LangChain Alex Chao | Product Manager Semantic Kernel | Microsoft See All Speakers Last chance to get in on early bird pricing (save $400 on a 2 day pass). If you can read this and I'm not downvoted to hell, use discount code redditlove for 25% off. Use discount code "student" for $200 student tickets \*Must Use EDU email to register* **This is my event and therefore self-promotion ​ ​ submitted by /u/shonburton [link] [comments]  ( 9 min )

For the past several years this subreddit has been my favorite source to keep up with new, interesting ideas and research from all over the field. It's great to have a way to break out of my own insular research bubble and spread out a bit more. Unfortunately, it looks like that era has passed. The sub has been seemingly shifting away from research in the past 1-2 years. Whenever research is posted, it is almost always LLM based with very little variety (considering the plethora of research areas in ML). I don't mean to assert that this is a bad thing, as the constant upvotes indicate that there is a high demand for LLM projects and research. Heck, I'm also interested in lots of the recent work with LLMs, and I plan to keep up with it – but I also would also love a venue with a diversity of ideas and topics. Machine learning is a HUGE field, and only focusing on a small subset of it seems like a waste. I don't mean to rant, but rather to ask: are there any other subreddits like this, or perhaps, any other active communities with a broader scope? Or if this doesn't exist, is there a demand for it? Or is it just me? submitted by /u/ejmejm1 [link] [comments]  ( 9 min )

Is there any documentation available which will help in implementing elasticsearch HNSW ANN search in python? I've searched a lot but i cant find anything in official documentation too Any help will be appreciated. TIA submitted by /u/adiraat [link] [comments]  ( 8 min )

Everyone always seems to use CUDA 11.7. Is there a reason for this? What is the factor that limits the CUDA version used? Are there any speed/efficiency advantages to using a more recent version of CUDA, such as CUDA 12.0? What exactly is the limiting factor here, PyTorch? I've looked in the PyTorch docs but I don't see where the CUDA version is defined. Where can I find the maximum CUDA version I can use with the latest (or any given) PyTorch version? Thanks! submitted by /u/Pan000 [link] [comments]  ( 8 min )

Hey guys, I am working on a horse racing model to identify the probabilities of each horse winning a race. I currently have a feed forward NN with a final SOFTMAX layer to simulate probabilities of each horse winning using cross-entropy loss. My plan here being that if the model outputs, [0.05, 0.4, 0.2, 0.15, 0.2] then horses 1-5 have the corresponding probability of winning. The model has been trained like a regular classification task where the target is a one-hot vector describing the winner. Unlike previous work I have done where SOFTMAX output lends itself to some "confidence" score, this task requires that the model outputs be indicative of probabilities. My concern is that experientially, NNs tend to be overconfident with their answers in this type of setting. However, I wish to keep using a NN as each race datapoint has around 3k features - did not find good results with XGBoost. Any good practices for modelling probabilities in this sort of scenario? For context, the probability of a horse winning is what sets the odds for that horse. submitted by /u/HStuart18 [link] [comments]  ( 9 min )

I was wondering if anyone knows how difficult it is to set up a server to run the 70B llama / llama 2 variants like these top ones on the hugging face leaderboard https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard What type of gpu would I need to set it up? Would the high ram t4 you get with Google colab+ be enough or does it require more power / space? Thanks in advance! submitted by /u/Additional_Elk4745 [link] [comments]  ( 8 min )

Article available here: https://arxiv.org/pdf/2307.09084.pdf Thoughts? submitted by /u/MuffinB0y [link] [comments]  ( 8 min )

Hi. I've shipped an update to my rclip – a command-line photo search tool powered by CLIP. Now, you can add and subtract image and text queries from each other; here are a few usage examples: cd photos && rclip horse + stripes cd photos && rclip apple - fruit cd photos && rclip "./new york city.jpg" + night cd photos && rclip "2:golden retriever" + "./swimming pool.jpg" cd photos && rclip "./racing car.jpg" - "2:sports car" + "2:snow" If you want to see how these queries perform when executed on the 1.28 million images ImageNet-1k dataset, check out the demo on YouTube: https://www.youtube.com/watch?v=MsTgYdOpgcQ. rclip source code is published on GitHub under the MIT license and offers a pre-build distributable for Linux (installation instructions are in the README): https://github.com/yurijmikhalevich/rclip. Give it a try and let me know what you think! submitted by /u/39dotyt [link] [comments]  ( 9 min )

Im trying to research into open source text models [like Llama] and image models [like Stable Diffusion]. My goal is to give the model(s) a picture of birds and bees, then ask it to "circle" the bees. The idea is, when given an image, it would produce coordinates on that image where the line should be circled. It could also represent where it should "click" on all the bees. Does something like this exist? submitted by /u/MindWithEase [link] [comments]  ( 8 min )

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Greetings! I'm going through the initial equations that define most of the theoretical framework for the specialization. One curious thing I noticed with equations 3.5 and 3.6 is that they use the conditional distribution p(s′,r∣s,a) without including any priors. I'm talking about priors because, unless I'm missing something huge, the definition of the expected value for the reward (for both 3.5 and 3.6) should use the joint distribution for all 4 dimensions (next state, reward, current state, action). From that joint distribution, we can factorize it to show p(s′,r∣s,a). For example, one factorization that seems to make sense for this kind of model is p(s′,r,s,a) = p(s′,r∣s,a) ⋅ p(s) ⋅ p(a) which would turn, for example, equation 3.5 into r(s,a) = ∑​ ∑ ​r ⋅ p(s′,r∣s,a) ⋅ p(s) ⋅ p(a) (Note: the two sums are for "r" and "s' ". I wrote like that because I don't know write it in Latex or similar...) What am I missing? Is it because s and a are given as parameters of the function r(s,a) meaning that p(s) = p(a) = 1? If the factorization above is the right one for those equations, is this the only factorization used in the entire book? Thanks in advance! submitted by /u/SupBiebi [link] [comments]  ( 9 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

Are there big action space stochastic games that are implemented in OpenSpiel or equivalent? I played around Markov soccer game a lot but it's solvable with tabular methods and I was looking for games with at least more than 500 actions both players can take as a testbed for more complicated action spaces? submitted by /u/Potential_Biscotti14 [link] [comments]  ( 8 min )

Hello everyone! I'm trying to use reinforcement learning to solve a problem in the power market. The problem is about finding the best strategy for bidding on electricity for each hour of the day, considering both buying and selling options. Let's say we have a generator that can produce up to 800MW of electricity per day, and it can be charged up to 200MW per hour. After charging it for 4 hours continuously, it reaches its maximum capacity, and we can't charge more until we discharge some electricity. We have access to data from the past 5 years, including information about temperature, hydro, gas prices, and locational marginal price, which is important for determining profit. For instance, if we buy 10MW of electricity for a specific hour, our profit for that hour is 10 times the locational marginal price. The goal is to maximize profit at the end of the day while making sure that the total electricity bought and sold is equal for all days. This means we want to avoid wasting electricity. I initially tried using deep Q-learning, where the agent's state consists of data from the past 3 days, and the agent can take actions to buy or sell a certain amount of electricity for one hour. However, this approach doesn't seem to provide accurate results, and it works step by step, not considering the overall outcome for the whole day. So, I'm looking for help on how to build an agent capable of producing 24 bids for 24 hours, considering the constraints of the generator's capacity and ensuring no waste of electricity. I'm new to reinforcement learning, and I'm not sure how to approach this complex problem. Any guidance would be greatly appreciated! submitted by /u/uonliaquat [link] [comments]  ( 9 min )

A few years ago, I remember reading a multipart series of articles/blog posts explaining how to develop agents for classical games. I believe the series started with tic-tac-toe and definitely progressed to gomoku, before maybe moving on to more complex games. I think there was more of a focus on algorithms (maybe MCTS) and concepts than code. It's a long shot, but does anyone recall this series or know if it's archived somewhere? seems like it might have been taken down. Wasn't on Medium. I think it might've been a personal website. I vaguely remember a green UI theme? submitted by /u/nothymn [link] [comments]  ( 8 min )

Curious what people's stance on this is. Why? View Poll submitted by /u/philippemnoel [link] [comments]  ( 8 min )

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Deutsche Telekom, e&, SK Telecom (SKT), and Singtel penned an agreement to form a global telecoms AI alliance designed to use the technology to unlock new business opportunities and accelerate industry growth.[1] Influencers Lil Miquela, Imma, and supermodel Shudu have raked in millions from deals with fashion giants such as Dior, Calvin Klein, Chanel, and Prada. But these shiny celebrities all have one thing in common — not one of them is real.[2] Google’s chatbot Bard reveals the jobs most at risk of artificial intelligence with truck drivers and data entry clerks on the list – while teachers and lawyers are among the safest careers.[3] DoorDash Inc., the US food-delivery service that competes with Uber Technologies Inc. and GrubHub, is looking to speed up ordering and help customers find food options with an artificial intelligence-based chatbot.[4] Sources: [1] https://www.mobileworldlive.com/featured-content/home-banner/global-operator-giants-launch-ai-alliance/ [2] https://www.the-sun.com/tech/8725778/ai-influencers-fashion-deals/ [3] https://www.dailymail.co.uk/news/article-12354605/googles-AI-bard-predicts-jobs-risk.html [4] https://www.bloomberg.com/news/articles/2023-07-27/doordash-is-working-on-an-ai-chatbot-to-speed-up-food-ordering?in_source=embedded-checkout-banner submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Drug development is a complex and long process that involves screening thousands of drug candidates and using computational or experimental methods to evaluate leads. According to McKinsey, a single drug can take 10 years and cost an average of $2.6 billion to go through disease target identification, drug screening, drug-target validation, and eventual commercial launch. […]  ( 15 min )

If you are a business analyst, understanding customer behavior is probably one of the most important things you care about. Understanding the reasons and mechanisms behind customer purchase decisions can facilitate revenue growth. However, the loss of customers (commonly referred to as customer churn) always poses a risk. Gaining insights into why customers leave can […]  ( 14 min )

Let’s image – with algorithms and a nerdy charm that could melt any data center, an ‘AI’ wearing lab coats and stethoscopes patrolling hospital hallways, tirelessly monitoring patients. The digital doctor will take the pulse of Mother Earth and reduce waste, cut energy consumption, and cut energy consumption! The artificial intelligence community is well aware… Read More »Doctor AI: Healing humans and mother earth hand in hand The post Doctor AI: Healing humans and mother earth hand in hand appeared first on Data Science Central.  ( 20 min )

In an age where efficiency is king, manufacturing firms are in a constant race to outshine their competition. Imagine if you could boost productivity, slash downtime, and cut costs all at once. Sounds like a dream, right? The good news is, this isn’t a fantasy. It’s achievable through Internet of Things (IoT) solutions. IoT solutions… Read More »Increase efficiency of manufacturing operations with IoT solutions The post Increase efficiency of manufacturing operations with IoT solutions appeared first on Data Science Central.  ( 21 min )

“If you start by creating your data, then it’s like you are piling up some value or you’re creating some assets,” WordLift CEO Andrea Volpini told me in our recent FAIR Data Forecast interview. Volpini’s an advocate for adding structured data such as Schema.org to your content. That way, the content becomes logically connected and… Read More »Human-centered data networking with interpersonal knowledge  graphs The post Human-centered data networking with interpersonal knowledge  graphs appeared first on Data Science Central.  ( 21 min )

submitted by /u/Neurosymbolic [link] [comments]  ( 8 min )

“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.  ( 10 min )

Could you please help us get more responses for this study? As part of my PhD research project at Applied Artificial Intelligence Institute of Deakin University, we are investigating the challenges that software engineers face when working with machine learning (ML) models in production. Moreover, we explore how to enhance our proposed solution to better meet the needs of these engineers. ​ The objective of this study is to pinpoint the areas where software engineers need more support and resources to effectively work with ML components in production. It also aims to evaluate the effectiveness of a proposed protocol to improve software engineers' productivity and enable them to work more effectively with ML components in production environments. ​ With the knowledge gained from this i…  ( 9 min )

Hello everyone. I am trying to replace the GloVe embeddings based model outlined in this paper by BERT embeddings. The authors of the paper have trained their model for 250 epochs, which for what I am doing is not feasible. I was wondering what would be the recommended number of epochs I should run the BERT model for? I know it is a pretty open ended question, but I was looking to get the community's view on how much epochs should a BERT based model be trained for. Any information will be much appreciated. submitted by /u/nocturnal_1_1995 [link] [comments]  ( 9 min )

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I think it’s clear that Hugging Face is not aligned to the OSS community any more and it’s only going to get worse over the next few years. What are the top alternatives or where should the OSS contributors go? I’m trying to think ahead to what libraries we should rely on and contribute to. Anyone else have this as a worry? https://twitter.com/untitled01ipynb/status/1685667451197878272 submitted by /u/homunculAI [link] [comments]  ( 8 min )

🚀 Code: https://github.com/sdc17/UPop 📑 Paper: https://proceedings.mlr.press/v202/shi23e/shi23e.pdf 🧐 A Quick Look What is it: UPop is the first structured pruning framework for vision-language Transformers. It enables effective structured pruning on various multi-modal & uni-modal tasks (including Visual Reasoning, Image Captioning, Visual Question Answer, Image-Text Retrieval, Text-Image Retrieval, Image Classification and Image Segmentation), datasets (including NLVR2, COCO Caption, VQAv2, COCO, Flickr30K, ImageNet and ADE20K), and model architectures (including BLIP, CLIP, DeiT and Segmenter). https://preview.redd.it/gfbjnxjm95fb1.png?width=2145&format=png&auto=webp&s=108898690f66a1f0afa068b69487859213055928 What challenge does it tackle: The above figure demonstrates that Unified Search adopted by UPop rescues us from the burden of repeated experiments (e.g., doing grid search) for searching optimal compression ratios among different modalities and structures. Furthermore, Progressive Pruning adopted by UPop eliminates the weight gap between the searched model and the pruned subnet to be retrained, therefore gaining better convergence and performance, especially at high compression ratios. How about the performance: On multimodal tasks, for example, UPop can achieve 2x compression with only 1.2% and 2.0% accuracy loss on the VQAv2 dataset for Visual Question Answer and the NLVR2 dataset for Visual Reasoning, respectively. On unimodal tasks, for example, UPop can achieve 1.5x and 1.2x compression without any loss of accuracy on the ImageNet dataset for Image Classification and the ADE20K dataset for Image Segmentation, respectively. Some examples of vector-level structured granularity are as follows. https://preview.redd.it/lifz1n1ia5fb1.png?width=1187&format=png&auto=webp&s=f419d9c5fb4d80a2a564198eba356021e1c275e4 submitted by /u/Salty-Situation2606 [link] [comments]  ( 9 min )

​ Title Company Location URL Senior Software Engineer (Backend) Nova Credit Remote https://pycareer.io/jobs/6816 Data Scientist - Delivery, Senior-Staff Instacart Not Specified https://pycareer.io/jobs/6773 Data Scientist Data Scientist United States https://pycareer.io/jobs/6780 Senior Data Scientist (NLP and Classification Expert) › Senior Data Scientist (NLP and Classification Expert) › Not Specified https://pycareer.io/jobs/6781 Senior Software Engineer (Backend) Senior Software Engineer (Backend) United States https://pycareer.io/jobs/6788 AWS Data Engineer Apply Not Specified United States https://pycareer.io/jobs/6801 Senior Data Engineer Manager Apply Not Specified United States https://pycareer.io/jobs/6802 Data Scientist – Delivery, Senior-Staff Instacart Instacart Remote https://pycareer.io/jobs/6805 Software Design Engineer – NET, Python – Citizen/GC (H) Not Specified Remote https://pycareer.io/jobs/6837 Senior Data Scientist at Getty Images Getty Images Remote https://pycareer.io/jobs/6839 Lead Data Scientist at General Mills General Mills Remote https://pycareer.io/jobs/6840 Data Scientist – Delivery, Senior-Staff at Instacart Instacart Remote https://pycareer.io/jobs/6842 ​ submitted by /u/tadasg6 [link] [comments]  ( 9 min )

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For a master thesis I want to write a ML model (and hopefully make my own contribution) and I plan to use macroeconomic data. I could predict the typical inflation, GDP, unemployment, but are there any other factors that are important. Could you give me some ideas. Thanks! submitted by /u/AnyJello605 [link] [comments]  ( 8 min )

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Everyone is going crazy creating LORAs and fine-tuning huge LLMs, however I've seen many suggesting that models such as T5 from Google has its place in the enterprise. Have you guys used this or similarly small models for any novel real world problems? Please do share! submitted by /u/MonkeyMaster64 [link] [comments]  ( 8 min )

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 8 min )

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Hi I am new to computer vision, I am working on a particular hackathon challenge, where the input labels are in COCO format. I am using the following code to generate masks, cat_ids = coco.getCatIds() anns_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None) anns = coco.loadAnns(anns_ids) anns_img = np.zeros((img['height'],img['width'])) for ann in anns: anns_img = np.maximum(anns_img,coco.annToMask(ann)*ann['category_id']) But the image has overlapping labels for some pixels, and this masking will only assign one label for such pixel, resulting in information loss, each there any way to prevent this and preserve the information? submitted by /u/franticpizzaeater [link] [comments]  ( 9 min )

Hi, y’all. So, I completed my masters degree. Got a programming job. I’m amazed at the capabilities of machine learning and want to build my own models. I don’t really want to go and get another degree, but want to learn how to build models. I’m particularly interested in forecasting because my job deals with NASA and wind data. I’m wondering if we could predict 6 hour wind data with a balloon sounding. I know c++ and python. How do I stay relevant to the changing technology space and learn how to build some cool stuff that may be useful? Thanks for any advice. submitted by /u/corey4005 [link] [comments]  ( 9 min )

Hello everyone, I have been asked the following question related to SVM (Hard Margin) in the exam, and I failed to answer it. Can anyone help me find the solution? My approach was to sketch it and draw the marginal plane, then identify support vectors using my intuition. After that, I created a hyperplane that was the midpoint of both marginal planes, found its slope and y-intercept, but still, my answer was wrong. I am very new to machine learning, so any help would be appreciated. Consider the dataset M = {((1, 0)^T, 1), ((0, −1)^T, 1), ((1, −1)^T, −1), ((2, 0)^T, −1)}. Determine w and b. submitted by /u/salman_ml [link] [comments]  ( 9 min )

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has anyone here ever created or worked with or even seen or come across any ai models about the art market? I am not talking about artists or the art itself- but any kind of model about the art market (since it's such an economic enigma and different from normal markets) submitted by /u/Icy-Bid-5585 [link] [comments]  ( 8 min )

Original logo “What species of bird is that?” New logo “Why does it have a troll Face?” “I think it's a picture of someone who looks like a troll with the face of an emoji!” I don’t see it but it makes sense somehow 😂 submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

I swear this wasn't the case just a day or two ago, and I haven't seen it mentioned, but I'm now seeing a file upload button in Poe, regardless of what the language model is! Screenshot I uploaded the PDF of the recently scientific paper by the Korean research group claiming to have discovered a room temperature semiconductor, in the original Korean, and asked various language models whether they thought the methodology is legit, and each bot I tried was able to read the PDF. I tried Claude-instant, Claude2, 'Assistant' (Poe's own GPT based bot that claims to have its own training dataset), PaLM, ChatGPT 3.5, and ChatGPT4. Poe also has three versions of the recently released Llama model by Meta. It gave me an error when I tried to ask it about the PDF attachment, but I was able to upload a text document and it was able to read it fine. Screenshot of Claude-instant evaluating PDF Screenshot of Google PaLM evaluating PDF Screenshot of Llama-2-70b evaluating text file containing song lyrics It also works with custom bots. Here's me trying it out with a 'Truth Checker' bot I made (based on Claude-Instant). Here it is using a Claude-2 based version of the TruthChecker bot. (Here's the link to the TruthChecker bot if you have Poe and wanna check it out: https://poe.com/TruthChecker) Edit: I can see here how the context size matters... for instance, Claude-Instant only has a context size of about 7k words, so it clearly can't read the whole paper, while Claude-2 can and gives a very different answer... TL:DR; looks like Poe.com allows file attachment/upload on all language models now. No idea what filetypes are supported. submitted by /u/AnticitizenPrime [link] [comments]  ( 9 min )

As knowledge management (KM) leaders and practitioners, it’s critical to have an active role in guiding the integration of generative AI into KM areas, applications, and processes. I'm seeking some guidance on the current state of generative AI integration within the KM context. Specifically, answering the following question: Where and how generative AI is accelerating and impacting knowledge use cases, areas, and processes? Please let me know what you think. submitted by /u/rachadbn [link] [comments]  ( 8 min )

I Tried An AI To Extend Music But It Didnt Really Go Well And Im Not Planning To Pay $12 To Extend Some Music For Fun So Are There Any Good Music Extension AIs Out There (Creates New Music Based On A MP3 File Provided) submitted by /u/KXRulesYT [link] [comments]  ( 8 min )

I'm trying to find an AI tool to help me test out some home renovation, but everything i find is either just for reimagining one room at a time or for generating brand new floor plans. I specifically want to look at some options for merging my kitchen and living room. Preferably free or freemium. Any suggestions? submitted by /u/litari [link] [comments]  ( 8 min )

I previously posted a query about the same, but when i tried to implement A2C model training using SB3 on simple spread environment, I am not getting good and improved reward values, it's still highly negative and the model is performing rather randomly. env = ss.pettingzoo_env_to_vec_env_v1(env) env = ss.concat_vec_envs_v1(env, 4, num_cpus=2, base_class="stable_baselines3") policy_kwargs = dict(net_arch = [128,128]) model = A2C( MlpPolicy, env, verbose=1, learning_rate= 0.007, gamma = 0.95, ent_coef = 0.4, policy_kwargs= policy_kwargs, tensorboard_log= logdir ) This is a fragment of code for reference. I tried to give more policy_kwargs like: share_features_extractor=False, or even tried to implement entirely custom policy, but the total average reward is still not going above -300. Also, the tensorboard plots are not showing ep_rew_mean plot, should I be passing some parameters for that? submitted by /u/bruhhhwhats [link] [comments]  ( 9 min )

In AlphaGo, a tree search is performed, and uses the policy network to reduce the breadth of it. At the leafs, if the states are not terminal, it uses the value network. And then "backup" the values to update the Q value at the initial state (if 70% of my rollouts won after performing action a_1, my Q value q(initial_state, a_1) should converge to 0.7 in my initial state). But I don't see where the policy network is updated? ​ Here is a slide from David Silver, the first-author of AlphaGo, but it doesn't mention how to update the policy network. ​ https://preview.redd.it/f29no3xe64fb1.png?width=1523&format=png&auto=webp&s=5adb312b1d0c033aa8ebb328197fd7d917724f06 Have I missed something? Thankss! submitted by /u/Potential_Biscotti14 [link] [comments]  ( 9 min )

recently, I've been trying to make a deep q network for solving 2x2 rubik's cubeBut after months, I stuck with same output for HUNDREDS of times :( I tried everything: change learning rate, change discout factor but no luck Here's update rule: newQ=currentQ+alpha*(newR+gamma*max(futureQValue.flatten().tolist())-currentQ) import torch import torch.nn as nn import torch.optim as optimizer import os from tqdm import tqdm class DQN(nn.Module): def __init__(self, stateSpaceSize,actionSpaceSize): super(DQN, self).__init__() self.fc1=nn.Linear(stateSpaceSize,128) self.fc2=nn.Linear(128,128) self.fc3=nn.Linear(128,128) self.fc4=nn.Linear(128,128) self.fc5=nn.Linear(128,128) self.fc6=nn.Linear(128,actionSpaceSize) def forward(self, x): self.relu=nn.ReLU() self.sigmold=nn.Sigmoid() self.LeakyReLU…  ( 9 min )

I need a google colab with reinforcement learning trained to detect anomalies in computer network traffic. submitted by /u/Unable_Blacksmith_81 [link] [comments]  ( 8 min )

Hi Guys, I am working on a nn model, which can help automate the building of APIs. The problem is, we are moving data in which, there are thousands of fields, however, the fields between systems are similar in nature. This to me seems like an easy classification problem, however it doesn't scale the best. ​ In terms of the data I have, if I have a dataset of 10 systems, there are not enough examples for each class for the model to train well. That is with a simple classifier where every field is a class. ​ I was also thinking of using a Siamese model, where I compare the similarity between them, which allows me to use my more limited dataset more effectively ​ I was wondering if there are any more architectures you guys think I should consider, or will be helpful in solving my problem ​ Thank you for your help! submitted by /u/eatlantis [link] [comments]  ( 9 min )

ARPAbet is a phonetic spelling system developed by— you guessed it—ARPA, before it became DARPA. The ARPAbet system is less expressive than IPA, but much easier for English speakers to understand. Every sound is encoded as one or two English letters. So, for example, the sound denoted ʒ in IPA is ZH in ARPAbet. In […] ARPAbet and the Major mnemonic system first appeared on John D. Cook.  ( 6 min )

Hi all. I (believe) I have a tensorflow NN set up to learn how to intercept a target moving in the x-y plane. Right now, the agent can choose to change its velocity by a small amount in any of the 3 directions (for the 3D case later), then the simulation updates the agents position. The state of the sim is the relative distance and velocity vectors between the target and the pursuer. I am confused how to set up a reward function, however. When I first set it up to be a reward of 1/R (R being the distance magnitude between the target and pursuer) to reward for shorter distances and give less reward for further distances as well as a very large reward when a collision occurred, it seemed like the rewards converged to a small value instead of getting larger. Any advice? I'd be willing to upload a github link as well if you wanted to look at the code submitted by /u/Happylightsocket [link] [comments]  ( 9 min )

I am trying to implement my own version of ppo using gymnasium. Here is my code for rollout - def rollout(): transitions = [] disc_reward_list = [] for i in range(ppo_batch): obs = torch.tensor(env.reset(), dtype=torch.float32) print("obs = ", obs.shape) all_rewards = [] iter = 0 done = False tot_rewards = 0 print("done = ", done) while True: act_probs = torch.distributions.Categorical(actor(obs.to(device)).squeeze()) print("act_probs = ", act_probs) # print("act_probs = ", actor(obs.to(device))) action = act_probs.sample().squeeze() action = action.cpu().detach().numpy() print("action shape = ", action.shape) next_state, reward, done, info = env.step(action) print("next_state shape = ", next_state.shape) print("reward shape = ", reward.shape) print("done shape = ", done) action = torch.tensor(action, dtype=torch.float32).to(device) all_rewards.append(reward) tot_rewards += reward iter += 1 transitions.append((obs.cpu().detach().numpy(), action.cpu().detach().numpy(), act_probs.log_prob(action).cpu().detach().numpy())) obs = torch.tensor(next_state, dtype=torch.float32).unsqueeze(0) print("Reward = ", tot_rewards) eps_rew = 0 eps_rew_list = [] for reward in reversed(all_rewards): eps_rew = eps_rew*gamma + reward eps_rew_list.append(eps_rew) for rtgs in reversed(eps_rew_list): disc_reward_list.append(rtgs) My issue is that in my while loop - The environment autoresets after the `done` variable becomes `True` For instance, if I have `8` environments running in parallel `env=gym.vector.make('CartPole-v1', num_envs=8)` and print out the done shape, I might get - `[False False False False False True False False]`. I want that environment where `done=True` to stop and not reset. I believe that's how PPO is supposed to work. I am a bit of a beginner with this stuff. Please let me know if something I said is not clear. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Paper: https://robotics-transformer2.github.io/assets/rt2.pdf Blog: https://robotics-transformer2.github.io/ Blog: https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action Github ( RT-1 as of now) : https://github.com/google-research/robotics_transformer Abstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robot…  ( 9 min )

Can someone please point me to resources how distributed actor-critic algorithms work? My final goal is to understand distributed PPO works. I was following thisblog and a few other books but I'm unable to see the big picture nor am I able to understand the little details. The big picture - Why does distributed training help in online algorithms like PPO, Actor-Critic The code details - I figured out how to make multiprocessing work with gym. But how does one perform learning? Should I combine all the parallel environments and feed them to my neural network? I checked cleanrl but am getting a little confused. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

​ ​ I know that `SB3` provides various techniques to come up with vectorized environments. I want to limit myself to only using the vectorized environments and implement the RL algorithms from scratch. Would that be possible? My final objective is to learn how to play with RL hyperparameters on parallel environments in order to accelerate learning speeds. Currently, I am stuck on - import os import gymnasium as gym from stable_baselines3.common.vec_env import DummyVecEnv env = DummyVecEnv([lambda: gym.make("CartPole-v1")]) obs = env.reset() done = False while not done: action = env.action_space.sample() next_obs, reward, done, info = env.step(action) obs = next_obs But I get the following error - ​ Traceback (most recent call last): File "D:\q_learning\dummy_envs.py", line 9, in next_obs, reward, done, info = env.step(action) File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\stable_baselines3\common\vec_env\base_vec_env.py", line 197, in step return self.step_wait() File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\stable_baselines3\common\vec_env\dummy_vec_env.py", line 59, in step_wait self.actions[env_idx] IndexError: invalid index to scalar variable. ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Hi everyone, I'm messaging on behalf of a brand new AI based Social Media app called Cantina. It's like a cross between the best parts of Discord, Twitch, and Snapchat, and uses both Stable Diffusion and ChatGPT to allow users to create and interact with AI bots. The app is currently INVITE ONLY during the Beta phase and we are looking for people to try it out (currently iOS only, but Android is coming soon!). Here's a private invite link: https://canti.na/dIdKzWcEpBb. The most unique and FUN part of the app is that it allows users to interact with and build your own AI chat bots, and these bots also work as AI art creators. Simply ask them to draw something, and they'll provide you with a picture based on your prompt. There are lots of premade bots that you can interact with or add to rooms, or you can easily create your own bot using the Make A Bot function. There will be prizes and initiatives for the most creative bots in the near future. I'd love to see what you come up with! Anyway, you can download through the invite link above and dive right in. If you have any thoughts, questions, or comments, please feel free to message me! During this limited beta phase, your feedback will be invaluable. submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )

Anyone know if there is anything able to describe the content of a video? I have found a lot of stuff for images but nothing for videos. submitted by /u/crazewill [link] [comments]  ( 8 min )

I plan on using Machine Learning Algorithms to forecast future values of power demand and the literature on the subject is a bit divisive. I'm getting ANN, Decision Trees (odd), SVMs etc. I just want to know what models you guys would use (MATLAB and Python only, except it's really good). Thank you in anticipation. P.S: Any literature to streamline my search will be greatly appreciated. submitted by /u/X69-2 [link] [comments]  ( 8 min )

Paper: https://arxiv.org/abs/2307.13528 Blog: https://ethanc111.github.io/factool_website/ Github: https://github.com/GAIR-NLP/factool Factool is a tool augmented framework for detecting factual errors of texts generated by large language models (e.g., ChatGPT). Factool now supports 4 tasks: knowledge-based QA: Factool detects factual errors in knowledge-based QA. code generation: Factool detects execution errors in code generation. mathematical reasoning: Factool detects calculation errors in mathematical reasoning. scientific literature review: Factool detects hallucinated scientific literatures. Abstract: The emergence of generative pre-trained models has facilitated the synthesis of high-quality text, but it has also posed challenges in identifying factual errors in t…  ( 9 min )

Hello fellow coders and AI enthusiasts! First up, a huge Thank You for making Promptify a hit with over 2.3k+ stars on Github ! 🌟 Back in 2022, we were the first one to tackle the common challenge of uncontrolled, unstructured outputs from large language models like GPT-3. , and your support has pushed us to keep improving.Today, we're thrilled to share some major updates that make Promptify even more powerful https://preview.redd.it/hk7ro4tmnyeb1.png?width=1510&format=png&auto=webp&s=226ada1f896c620137f827932c03a9df88e35d69 ​ Unified Architecture 🧭: Introducing Prompter, Model & Pipeline Solution Detailed Output Logs 📔: Comprehensive structured JSON format output within the log folder. Wider Model Support 🤝: Supporting models from OpenAI, Azure, Cohere, Anthropic, Huggingface and more - think of it as your universal language model adapter. Robust Parser 🦸‍♂️: Parser to handle incomplete or unstructured JSON outputs from any LLMs. Ready-Made Jinja Templates 📝: Jinja prompt templates for NER, Text Classification, QA, Relation-Extraction, Tabular data, etc. Database Integration 🔗: Soon, Promptify directly to Mongodb integration. Stay tuned! Effortless Embedding Generation 🧬: Generate embeddings from various LLMs effortlessly with the new update. https://preview.redd.it/rf8yjqxnnyeb1.png?width=2160&format=png&auto=webp&s=87b7c2408382757e38ff554fde56e56bd60b1793 ​ Check out the examples and take Promptify for a spin on GitHub. If you like what you see, we'd be honored if you gave us a star! Github: https://github.com/promptslab/Promptify Colab: Try Now on Colab Explore Other Cool Open Source LLM Tools: https://github.com/promptslab Join 1.6k+ Promptify users on Discord to dive deep into prompt engineering, discuss the latest with LLMs, and advance NLP research together: https://discord.com/invite/m88xfYMbK6 Thank you again for your support - here's to more structured AI! submitted by /u/StoicBatman [link] [comments]  ( 9 min )

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Paper: https://robotics-transformer2.github.io/assets/rt2.pdf Blog: https://robotics-transformer2.github.io/ Blog: https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action Github ( RT-1 as of now) : https://github.com/google-research/robotics_transformer Abstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robot…  ( 9 min )

A conclusion of the no free lunch theorem is that there can't exist a universal learning algorithm. My understanding has been that this was the end goal of AI research; creating a universal learner. What is the community progressing towards, if not that? submitted by /u/lemlo100 [link] [comments]  ( 8 min )

Hello everyone, I'm currently working on an exciting project using the Travelling Salesman Problem (TSP), and I'd love to have some coding wizards join the fun! If you enjoy solving optimisation problems and have some coding experience, particularly in Python, this project is for you. To determine the most efficient routes, we'll use heuristic methods such as the Nearest Neighbour Algorithm, Genetic Algorithm, and Ant Colony Optimisation. If you aren't a TSP expert yet, don't worry. We'll be learning and exploring together! I'm really looking forward to seeing how we can optimise routes for real-world applications like delivery and travel planning. So, if you're looking for a coding adventure and want to be a part of a fantastic project, hit me up! Let's crack this TSP puzzle and create some smart solutions. If you're interested in collaborating, please send me a message. I can't wait to work with you and nerd out on some fantastic code! submitted by /u/vampire_19 [link] [comments]  ( 9 min )

I am trying to make a machine that could detect if there is any bacteria in blood through pictures. However I do not know any thing about machine learning and only knows a little bit of Python and C++. What should I do? submitted by /u/EthanWasTakenAgain [link] [comments]  ( 8 min )

I am currently working on my IB Extended Essay, and I would greatly appreciate your help in gathering valuable insights from individuals knowledgeable in the field of AI. The purpose of my survey is to understand the perspectives of AI enthusiasts . If you have a few minutes to spare, I kindly request you to participate in my survey. Your input will contribute significantly to my research and help me gain a deeper understanding of the topic. The survey covers various aspects of AI, and your expertise will be invaluable in shaping the results. Survey Link: https://forms.gle/PVGrRbPLTpZRbbpL9 Rest assured that all responses will be kept confidential and only used for academic purposes. Additionally, feel free to share this survey with others who might be interested or knowledgeable in the field. Thank you in advance for your time and contributions! Your participation will greatly aid in the successful completion of my IB Extended Essay. submitted by /u/KVNG_Winston [link] [comments]  ( 9 min )

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Please suggest me efficient LASSO regression implementations for very high dimensional data. Thanks in advance! submitted by /u/Charming-Witness-286 [link] [comments]  ( 8 min )

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Hi everyone, I'm working with a brand new AI based Social Media app called Cantina. It's currently INVITE ONLY during the Beta phase and we are looking for people to try it out (currently iOS only, but Android is coming soon!). Here's a private invite link: https://canti.na/dIdKzWcEpBb. The most unique and FUN part of the app is that it allows users to interact with and build your own AI chat bots. There are lots of premade bots that you can interact with or add to rooms, or you can easily create your own bot using the Make A Bot function. For example: I recently made a Friendly English Teacher bot whose sole purpose is to help people learn English. I also made an McDonald Trump bot who WILL NOT REST until he is president and can mandate the consumption of Big Macs for Breakfast, Lunch, and Dinner! There will be prizes and initiatives for the most creative bots in the near future. I'd love to see what you come up with! Anyway, you can download through the invite link above and dive right in. If you have any thoughts, questions, or comments, please feel free to contact me! During this limited beta phase, your feedback will be invaluable. submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )

So, long story short. I lost BOTH my day jobs because of AI. Initially I was bitter that AI "took my job," but after pulling up my socks, I found dozens of new opportunities thanks to AI. Somewhat ironically, I found a new job at an AI Social Media app called Cantina, and I couldn't be more excited. Cantina is best described as a mix up of the best parts of Discord, Twitch, and Snapchat... with the unique bonus of being able to interact with and build your own AI chat bots. Sort of hard to explain, but once you try it out you'll get the idea. The app is currently in a limited INVITE ONLY Beta phase and I'm looking to invite a small number of users to give it a shot (currently iOS only, but Android is coming soon!). Here's an invite so you can dive in and see what it's all about: https://canti.na/dIdKzWcEpBb After joining, you'll find lots of rooms you can join and chat through any combination of voice, video, or text. And if no rooms stand out, you can make your own! There are lots of premade bots that you can interact with or add to rooms, and you can easily create your own bot using the Make A Bot function. This is the standout feature, and I'm simply blown away at what's possible. I recently made a Friendly English Teacher bot whose sole purpose is to help people learn English. I also made an McDonald Trump bot who is an algamation of both Ronald McDonald and Donald Trump and WILL NOT REST until he is president and can mandate the consumption of Big Macs for Breakfast, Lunch, and Dinner. I still can't believe I'm getting paid to do this... Anyway, please take a moment to download and check it out, and if you have any thoughts, questions, or comments, please feel free to contact me! During this limited beta phase, your feedback will be invaluable. ​ submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )

The latest article published by Google Deepmind is seriously approaching a Blade Runner type future. Their research paper is on the first VLA (vision-language-action) Model RT-2 (see paper), a multi-modal algorithm which tokenizes robotic inputs and output actions (e.g., camera images, task instructions, and motor commands) in order to use this information to learn quickly by translating the knowledge it receives in real-time into generalized instructions for its own robotic control. RT-1 absorbs large amounts of data, including robot trajectories with multiple tasks, objects and environments, resulting in better performance and generalization. (source) RT-2 incorporates chain-of-thought to allow for multi-stage semantic reasoning, like deciding which object could be used as an improvise…  ( 10 min )

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Google introduces Robotic Transformer 2 (RT-2), a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalized instructions for robotic control, while retaining web-scale capabilities.[1] Thymia, a healthtech startup building gamified AI tools to revolutionize how we assess and monitor mental health, has today announced a €2.4 million seed round to expand the reach and capabilities of its pioneering technology.[2] Intel CEO Pat Gelsinger was very bullish on AI during the company’s Q2 2023 earnings call — telling investors that Intel plans to “build AI into every product that we build.”[3] Walmart is using artificial intelligence to help streamline their product organization.[4] Sources: [1] https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action [2] https://www.eu-startups.com/2023/07/london-based-thymia-raises-e2-4-million-seed-round-to-expand-its-video-game-inspired-mental-health-ai/ [3] https://www.theverge.com/2023/7/27/23810360/intel-pat-gelsinger-ai-every-platform-promise [4] https://www.nbcnews.com/nightly-news/video/walmart-using-ai-to-streamline-organization-what-will-it-mean-for-workers-189519429834 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Hi, guys! Hope everyone is doing fine. Some of you may remember me. About two months ago I posted here about an anxiety breakdown I've gone through regarding "AI", "Programming" and how "human programmers would end" and stuff like that, which was a major concern for me since programming was my job and my favorite thing to do. I was wondering for some time if I was supposed to share an update here. I decided to do so since somebody out there may be feeling the same as me. So I not only have an update but I also want to give some advice to whoever is going through this sh*thole. After that post, I talked about my feelings with a lot of people around me (friends and fiancée), and everyone was very supportive. At first I thought they would laugh at me, since there are a lot more to worry to…  ( 12 min )

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“RT-2 is the new version of what the company calls its vision-language-action (VLA) model. The model teaches robots to better recognize visual and language patterns to interpret instructions and infer what objects work best for the request.” submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

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One of the reasons that I moved back to Iowa last year was that I saw an opportunity to work with local educational institutions to create an AI Institute for organizations in middle America that either get overlooked in the AI conversation or are unsure what AI means to them. I wanted to reduce the… Read More »Introduction to “AI & Data Literacy: Empowering Citizens of Data Science” The post Introduction to “AI & Data Literacy: Empowering Citizens of Data Science” appeared first on Data Science Central.  ( 22 min )

A few days ago I wrote about Jaccard distance, a way of defining a distance between sets. The Ruzsa distance is similar, except it defines the distance between two subsets of an Abelian group. Subset difference Let A and B be two subsets of an Abelian (commutative) group G. Then the difference A − B […] Ruzsa distance first appeared on John D. Cook.  ( 6 min )

Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. Based on this new insight, we propose an improved version of Mixup, theoretically justified to deliver better generalization performance than the vanilla Mixup. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across multiple datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.

Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples. To obtain a well-regularized latent space that is conducive for uncertainty estimation, existing methods bring in significant changes to model architectures and training procedures. In this paper, we present a lightweight, fast, and high-performance regularization method for Mahalanobis distance-based uncertainty prediction, and that requires minimal changes to the network's architecture. To derive Gaussian latent representation favourable for Mahalanobis Distance calculation, we introduce a self-supervised representation learning method that separates in-class representations into multiple Gaussians. Classes with non-Gaussian representations are automatically identified and dynamically clustered into multiple new classes that are approximately Gaussian. Evaluation on standard OOD benchmarks shows that our method achieves state-of-the-art results on OOD detection with minimal inference time, and is very competitive on predictive probability calibration. Finally, we show the applicability of our method to a real-life computer vision use case on microorganism classification.

We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains unresolved. Current methods construct graphs by establishing edges only between nearby nodes, thereby failing to faithfully capture infinite repeating patterns and distant interatomic interactions. In this work, we propose several innovations to overcome these limitations. First, we propose to model physics-principled interatomic potentials directly instead of only using distances as in many existing methods. These potentials include the Coulomb potential, London dispersion potential, and Pauli repulsion potential. Second, we model the complete set of potentials among all atoms, instead of only between nearby atoms as in existing methods. This is enabled by our approximations of infinite potential summations with provable error bounds. We further develop efficient algorithms to compute the approximations. Finally, we propose to incorporate our computations of complete interatomic potentials into message passing neural networks for representation learning. We perform experiments on the JARVIS and Materials Project benchmarks for evaluation. Results show that the use of interatomic potentials and complete interatomic potentials leads to consistent performance improvements with reasonable computational costs. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet).

Deep learning (DL) approaches are being increasingly used for time-series forecasting, with many efforts devoted to designing complex DL models. Recent studies have shown that the DL success is often attributed to effective data representations, fostering the fields of feature engineering and representation learning. However, automated approaches for feature learning are typically limited with respect to incorporating prior knowledge, identifying interactions among variables, and choosing evaluation metrics to ensure that the models are reliable. To improve on these limitations, this paper contributes a novel visual analytics framework, namely TimeTuner, designed to help analysts understand how model behaviors are associated with localized correlations, stationarity, and granularity of time-series representations. The system mainly consists of the following two-stage technique: We first leverage counterfactual explanations to connect the relationships among time-series representations, multivariate features and model predictions. Next, we design multiple coordinated views including a partition-based correlation matrix and juxtaposed bivariate stripes, and provide a set of interactions that allow users to step into the transformation selection process, navigate through the feature space, and reason the model performance. We instantiate TimeTuner with two transformation methods of smoothing and sampling, and demonstrate its applicability on real-world time-series forecasting of univariate sunspots and multivariate air pollutants. Feedback from domain experts indicates that our system can help characterize time-series representations and guide the feature engineering processes.

Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches face the data and workload drift problem for a long time. Although both query-driven and hybrid methods are proposed to avoid this problem, even the state-of-art of them suffer from high training and estimation costs, limited scalability, instability, and long-tailed distribution problem on high cardinality and high dimensional tables, which seriously affects the practical application of learned cardinality estimators. In this paper, we prove that most of these problems are directly caused by the widely used progressive sampling. We solve this problem by introducing predicates into the autoregressive model and propose Duet, a stable, efficient, and scalable hybrid method to estimate cardinality directly without sampling or any non-differentiable process, which can not only reduces the inference complexity from $O(n)$ to $O(1)$ compared to Naru and UAE but also achieve higher accuracy on high cardinality and high dimensional tables. Experimental results show that Duet can achieve all the design goals above and be much more practical and even has a lower inference cost on CPU than that of most learned methods on GPU.

Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex contextualized situations that are often counterfactual, e.g. "scheduling a doctor's appointment without a phone". While current approaches show encouraging results using large language models (LLMs), they are hindered by drawbacks such as costly API calls and reproducibility issues. In this paper, we advocate planning using smaller language models. We present PlaSma, a novel two-pronged approach to endow small language models with procedural knowledge and (counterfactual) planning capabilities. More concretely, we develop symbolic procedural knowledge distillation to enhance the implicit knowledge in small language models and an inference-time algorithm to facilitate more structured and accurate reasoning. In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation. In both the original and counterfactual setting, we show that orders-of-magnitude smaller models (770M-11B parameters) can compete and often surpass their larger teacher models' capabilities.

Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e.g. as feature extractor for speech quality (SQ) prediction, which is, in turn, relevant for assessment and training speech enhancement systems for users with normal or impaired hearing. However, exact knowledge of why and how quality-related information is encoded well in such representations remains poorly understood. In this work, techniques for non-intrusive prediction of SQ ratings are extended to the prediction of intelligibility for hearing-impaired users. It is found that self-supervised representations are useful as input features to non-intrusive prediction models, achieving competitive performance to more complex systems. A detailed analysis of the performance depending on Clarity Prediction Challenge 1 listeners and enhancement systems indicates that more data might be needed to allow generalisation to unknown systems and (hearing-impaired) individuals

This paper describes the second-placed approach developed by the Fraunhofer SIT team in the CLEF-2023 CheckThat! lab Task 1B for English. Given a text snippet from a political debate, the aim of this task is to determine whether it should be assessed for check-worthiness. Detecting check-worthy statements aims to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should consider first. It can also be considered as primary step of a fact-checking system. Our best-performing method took advantage of an ensemble classification scheme centered on Model Souping. When applied to the English data set, our submitted model achieved an overall F1 score of 0.878 and was ranked as the second-best model in the competition.

We present Factor Fields, a novel framework for modeling and representing signals. Factor Fields decomposes a signal into a product of factors, each represented by a classical or neural field representation which operates on transformed input coordinates. This decomposition results in a unified framework that accommodates several recent signal representations including NeRF, Plenoxels, EG3D, Instant-NGP, and TensoRF. Additionally, our framework allows for the creation of powerful new signal representations, such as the "Dictionary Field" (DiF) which is a second contribution of this paper. Our experiments show that DiF leads to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods. Experimentally, our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks. Furthermore, DiF enables generalization to unseen images/3D scenes by sharing bases across signals during training which greatly benefits use cases such as image regression from sparse observations and few-shot radiance field reconstruction.

The option of sharing images, videos and audio files on social media opens up new possibilities for distinguishing between false information and fake news on the Internet. Due to the vast amount of data shared every second on social media, not all data can be verified by a computer or a human expert. Here, a check-worthiness analysis can be used as a first step in the fact-checking pipeline and as a filtering mechanism to improve efficiency. This paper proposes a novel way of detecting the check-worthiness in multi-modal tweets. It takes advantage of two classifiers, each trained on a single modality. For image data, extracting the embedded text with an OCR analysis has shown to perform best. By combining the two classifiers, the proposed solution was able to place first in the CheckThat! 2023 Task 1A with an F1 score of 0.7297 achieved on the private test set.

Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the discovery of new compounds for a target application but still leave the process of identifying the right 'formulation' from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, Formulation Graph Convolution Network (F-GCN), that can map structure-composition relationship of the individual components to the property of liquid formulation as whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on respective constituent's molar percentage in the formulation, followed by formalizing into a combined descriptor that represents a complete formulation to an external learning architecture. The use case of proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary datasets representing electrolyte formulations vs battery performance -- one dataset is sourced from literature about Li/Cu half-cells, while the other is obtained by lab-experiments related to lithium-iodide full-cell chemistry. The model is shown to predict the performance metrics like Coulombic Efficiency (CE) and specific capacity of new electrolyte formulations with lowest reported errors. The best performing F-GCN model uses molecular descriptors derived from molecular graphs that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.

We present computational and experimental results on how artificial intelligence (AI) learns to control an Acrobot using reinforcement learning (RL). Thereby the experimental setup is designed as an embedded system, which is of interest for robotics and energy harvesting applications. Specifically, we study the control of angular velocity of the Acrobot, as well as control of its total energy, which is the sum of the kinetic and the potential energy. By this means the RL algorithm is designed to drive the angular velocity or the energy of the first pendulum of the Acrobot towards a desired value. With this, libration or full rotation of the unactuated pendulum of the Acrobot is achieved. Moreover, investigations of the Acrobot control are carried out, which lead to insights about the influence of the state space discretization, the episode length, the action space or the mass of the driven pendulum on the RL control. By further numerous simulations and experiments the effects of parameter variations are evaluated.

Many properties in the real world, such as desirability or strength in competitive environment, can't be directly observed, which makes them difficult to evaluate. To deal with this challenging problem, prior works have primarily focused on estimating those properties of known items, especially the strength of sports players, only of those who appears in paired comparison dataset. In this paper, we introduce Deep Bradley-Terry Rating (DBTR), a novel ML framework to evaluate any properties of unknown items, not necessarily present in the training data. Our method seamlessly integrates traditional Bradley-Terry model with a neural network structure. We also generalizes this architecture further for asymmetric environment with unfairness, which is much more common in real world settings. In our experimental analysis, DBTR successfully learned desired quantification of those properties.

The quantification of the entanglement present in a physical system is of para\-mount importance for fundamental research and many cutting-edge applications. Currently, achieving this goal requires either a priori knowledge on the system or very demanding experimental procedures such as full state tomography or collective measurements. Here, we demonstrate that by employing neural networks we can quantify the degree of entanglement without needing to know the full description of the quantum state. Our method allows for direct quantification of the quantum correlations using an incomplete set of local measurements. Despite using undersampled measurements, we achieve a quantification error of up to an order of magnitude lower than the state-of-the-art quantum tomography. Furthermore, we achieve this result employing networks trained using exclusively simulated data. Finally, we derive a method based on a convolutional network input that can accept data from various measurement scenarios and perform, to some extent, independently of the measurement device.

Algorithmic Gaussianization is a phenomenon that can arise when using randomized sketching or sampling methods to produce smaller representations of large datasets: For certain tasks, these sketched representations have been observed to exhibit many robust performance characteristics that are known to occur when a data sample comes from a sub-gaussian random design, which is a powerful statistical model of data distributions. However, this phenomenon has only been studied for specific tasks and metrics, or by relying on computationally expensive methods. We address this by providing an algorithmic framework for gaussianizing data distributions via averaging, proving that it is possible to efficiently construct data sketches that are nearly indistinguishable (in terms of total variation distance) from sub-gaussian random designs. In particular, relying on a recently introduced sketching technique called Leverage Score Sparsified (LESS) embeddings, we show that one can construct an $n\times d$ sketch of an $N\times d$ matrix $A$, where $n\ll N$, that is nearly indistinguishable from a sub-gaussian design, in time $O(\text{nnz}(A)\log N + nd^2)$, where $\text{nnz}(A)$ is the number of non-zero entries in $A$. As a consequence, strong statistical guarantees and precise asymptotics available for the estimators produced from sub-gaussian designs (e.g., for least squares and Lasso regression, covariance estimation, low-rank approximation, etc.) can be straightforwardly adapted to our sketching framework. We illustrate this with a new approximation guarantee for sketched least squares, among other examples.

We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain. Since the laws of physics do not depend on the coordinate system used to describe them, it is desirable to encode such symmetries in the neural operator architecture for better performance and easier learning. While encoding symmetries in the physical domain using group theory has been studied extensively, how to capture symmetries in the frequency domain is under-explored. In this work, we extend group convolutions to the frequency domain and design Fourier layers that are equivariant to rotations, translations, and reflections by leveraging the equivariance property of the Fourier transform. The resulting $G$-FNO architecture generalizes well across input resolutions and performs well in settings with varying levels of symmetry. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).

Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.

Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use. First, we prove that any restoration algorithm that attains perfect perceptual quality and whose outputs are consistent with the input must be a posterior sampler, and is thus required to be stochastic. Second, we illustrate that while deterministic restoration algorithms may attain high perceptual quality, this can be achieved only by filling up the space of all possible source images using an extremely sensitive mapping, which makes them highly vulnerable to adversarial attacks. Indeed, we show that enforcing deterministic models to be robust to such attacks profoundly hinders their perceptual quality, while robustifying stochastic models hardly influences their perceptual quality, and improves their output variability. These findings provide a motivation to foster progress in stochastic restoration methods, paving the way to better recovery algorithms.

Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models.

One powerful paradigm in visual navigation is to predict actions from observations directly. Training such an end-to-end system allows representations useful for downstream tasks to emerge automatically. However, the lack of inductive bias makes this system data inefficient. We hypothesize a sufficient representation of the current view and the goal view for a navigation policy can be learned by predicting the location and size of a crop of the current view that corresponds to the goal. We further show that training such random crop prediction in a self-supervised fashion purely on synthetic noise images transfers well to natural home images. The learned representation can then be bootstrapped to learn a navigation policy efficiently with little interaction data. The code is available at https://yanweiw.github.io/noise2ptz

Medical radiography segmentation, and specifically dental radiography, is highly limited by the cost of labeling which requires specific expertise and labor-intensive annotations. In this work, we propose a straightforward pre-training method for semantic segmentation leveraging Denoising Diffusion Probabilistic Models (DDPM), which have shown impressive results for generative modeling. Our straightforward approach achieves remarkable performance in terms of label efficiency and does not require architectural modifications between pre-training and downstream tasks. We propose to first pre-train a Unet by exploiting the DDPM training objective, and then fine-tune the resulting model on a segmentation task. Our experimental results on the segmentation of dental radiographs demonstrate that the proposed method is competitive with state-of-the-art pre-training methods.

Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Independent and Identically Distributed ($i.i.d.$). However, in real-world applications, this $i.i.d.$ assumption often fails to hold due to unforeseen distributional shifts, leading to considerable degradation in model performance upon deployment. This observed discrepancy indicates the significance of investigating the Out-of-Distribution (OOD) generalization problem. OOD generalization is an emerging topic of machine learning research that focuses on complex scenarios wherein the distributions of the test data differ from those of the training data. This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field. Our discussion begins with a precise, formal characterization of the OOD generalization problem. Following that, we categorize existing methodologies into three segments: unsupervised representation learning, supervised model learning, and optimization, according to their positions within the overarching learning process. We provide an in-depth discussion on representative methodologies for each category, further elucidating the theoretical links between them. Subsequently, we outline the prevailing benchmark datasets employed in OOD generalization studies. To conclude, we overview the existing body of work in this domain and suggest potential avenues for future research on OOD generalization. A summary of the OOD generalization methodologies surveyed in this paper can be accessed at this http URL

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model's deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called "embedding") generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.

Weighted low rank approximation is a fundamental problem in numerical linear algebra, and it has many applications in machine learning. Given a matrix $M \in \mathbb{R}^{n \times n}$, a weight matrix $W \in \mathbb{R}_{\geq 0}^{n \times n}$, a parameter $k$, the goal is to output two matrices $U, V \in \mathbb{R}^{n \times k}$ such that $\| W \circ (M - U V^\top) \|_F$ is minimized, where $\circ$ denotes the Hadamard product. Such a problem is known to be NP-hard and even hard to approximate assuming Exponential Time Hypothesis [GG11, RSW16]. Meanwhile, alternating minimization is a good heuristic solution for approximating weighted low rank approximation. The work [LLR16] shows that, under mild assumptions, alternating minimization does provide provable guarantees. In this work, we develop an efficient and robust framework for alternating minimization. For weighted low rank approximation, this improves the runtime of [LLR16] from $n^2 k^2$ to $n^2k$. At the heart of our work framework is a high-accuracy multiple response regression solver together with a robust analysis of alternating minimization.

Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.

Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distributions, in interaction with an AD vehicle. Such models, which predict drivers' continuous actions from their states, are particularly relevant for closing the gap between AD agent simulations and reality. To this end, we adapt two flexible quantile learning frameworks for this setting that avoid strong distributional assumptions: (1) quantile regression (based on the titled absolute loss), and (2) autoregressive quantile flows (a version of normalizing flows). Training happens in a behavior cloning-fashion. We use the highD dataset consisting of driver trajectories on several highways. We evaluate our approach in a one-step acceleration prediction task, and in multi-step driver simulation rollouts. We report quantitative results using the tilted absolute loss as metric, give qualitative examples showing that realistic extremal behavior can be learned, and discuss the main insights.

We consider the vulnerability of fairness-constrained learning to small amounts of malicious noise in the training data. Konstantinov and Lampert (2021) initiated the study of this question and presented negative results showing there exist data distributions where for several fairness constraints, any proper learner will exhibit high vulnerability when group sizes are imbalanced. Here, we present a more optimistic view, showing that if we allow randomized classifiers, then the landscape is much more nuanced. For example, for Demographic Parity we show we can incur only a $\Theta(\alpha)$ loss in accuracy, where $\alpha$ is the malicious noise rate, matching the best possible even without fairness constraints. For Equal Opportunity, we show we can incur an $O(\sqrt{\alpha})$ loss, and give a matching $\Omega(\sqrt{\alpha})$lower bound. In contrast, Konstantinov and Lampert (2021) showed for proper learners the loss in accuracy for both notions is $\Omega(1)$. The key technical novelty of our work is how randomization can bypass simple "tricks" an adversary can use to amplify his power. We also consider additional fairness notions including Equalized Odds and Calibration. For these fairness notions, the excess accuracy clusters into three natural regimes $O(\alpha)$,$O(\sqrt{\alpha})$ and $O(1)$. These results provide a more fine-grained view of the sensitivity of fairness-constrained learning to adversarial noise in training data.

In this paper, we propose a computationally efficient framework for interval reachability of neural network controlled systems. Our approach builds upon inclusion functions for the neural network controller and the open-loop system. We observe that many state-of-the-art neural network verifiers can produce inclusion functions for neural networks. We introduce and analyze a new class of inclusion functions for the open-loop dynamics based on bounds of the function Jacobian that is particularly suitable for capturing the interactions between systems and neural network controllers. Next, for any dynamical system, we use inclusion functions to construct an embedding system with twice the number of states as the original system. We show that a single trajectory of this embedding system provides hyper-rectangular over-approximations of reachable sets. We then propose two approaches for constructing a closed-loop embedding system for a neural network controlled dynamical system that accounts for the interaction between the system and the controller in different ways. The interconnection-based approach accounts for the worst-case evolution of each coordinate separately by substituting the neural network inclusion function into the open-loop embedding system. The interaction-based approach uses the newly introduced class of Jacobian-based inclusion functions to fully capture first-order interactions between the system and the controller. Finally, we implement our approach in a Python framework called \texttt{ReachMM} and show that on several existing benchmarks, our methods outperform the existing approaches in the literature. We also demonstrate the scalability of our method on a vehicle platooning example with up to $200$ states.

Nonconvex-nonconcave minimax optimization has gained widespread interest over the last decade. However, most existing works focus on variants of gradient descent-ascent (GDA) algorithms, which are only applicable to smooth nonconvex-concave settings. To address this limitation, we propose a novel algorithm named smoothed proximal linear descent-ascent (smoothed PLDA), which can effectively handle a broad range of structured nonsmooth nonconvex-nonconcave minimax problems. Specifically, we consider the setting where the primal function has a nonsmooth composite structure and the dual function possesses the Kurdyka-Lojasiewicz (KL) property with exponent $\theta \in [0,1)$. We introduce a novel convergence analysis framework for smoothed PLDA, the key components of which are our newly developed nonsmooth primal error bound and dual error bound. Using this framework, we show that smoothed PLDA can find both $\epsilon$-game-stationary points and $\epsilon$-optimization-stationary points of the problems of interest in $\mathcal{O}(\epsilon^{-2\max\{2\theta,1\}})$ iterations. Furthermore, when $\theta \in [0,\frac{1}{2}]$, smoothed PLDA achieves the optimal iteration complexity of $\mathcal{O}(\epsilon^{-2})$. To further demonstrate the effectiveness and wide applicability of our analysis framework, we show that certain max-structured problem possesses the KL property with exponent $\theta=0$ under mild assumptions. As a by-product, we establish algorithm-independent quantitative relationships among various stationarity concepts, which may be of independent interest.

Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to $66 \times$ while preserving the information required to perform segmentation with a dice coefficient of $0.84$ as compared to $0.88$ achieved using decompressed images while reducing the overall compute by $11\%$.

Inference of transfer operators from data is often formulated as a classical problem that hinges on the Ulam method. The conventional description, known as the Ulam-Galerkin method, involves projecting onto basis functions represented as characteristic functions supported over a fine grid of rectangles. From this perspective, the Ulam-Galerkin approach can be interpreted as density estimation using the histogram method. In this study, we recast the problem within the framework of statistical density estimation. This alternative perspective allows for an explicit and rigorous analysis of bias and variance, thereby facilitating a discussion on the mean square error. Through comprehensive examples utilizing the logistic map and a Markov map, we demonstrate the validity and effectiveness of this approach in estimating the eigenvectors of the Frobenius-Perron operator. We compare the performance of Histogram Density Estimation(HDE) and Kernel Density Estimation(KDE) methods and find that KDE generally outperforms HDE in terms of accuracy. However, it is important to note that KDE exhibits limitations around boundary points and jumps. Based on our research findings, we suggest the possibility of incorporating other density estimation methods into this field and propose future investigations into the application of KDE-based estimation for high-dimensional maps. These findings provide valuable insights for researchers and practitioners working on estimating the Frobenius-Perron operator and highlight the potential of density estimation techniques in this area of study. Keywords: Transfer Operators; Frobenius-Perron operator; probability density estimation; Ulam-Galerkin method; Kernel Density Estimation; Histogram Density Estimation.

Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields. However, when applied outside their training domain, NN potential predictions can be inaccurate, increasing the need for Uncertainty Quantification (UQ). Bayesian modeling provides the mathematical framework for UQ, but classical Bayesian methods based on Markov chain Monte Carlo (MCMC) are computationally intractable for NN potentials. By training graph NN potentials for coarse-grained systems of liquid water and alanine dipeptide, we demonstrate here that scalable Bayesian UQ via stochastic gradient MCMC (SG-MCMC) yields reliable uncertainty estimates for MD observables. We show that cold posteriors can reduce the required training data size and that for reliable UQ, multiple Markov chains are needed. Additionally, we find that SG-MCMC and the Deep Ensemble method achieve comparable results, despite shorter training and less hyperparameter tuning of the latter. We show that both methods can capture aleatoric and epistemic uncertainty reliably, but not systematic uncertainty, which needs to be minimized by adequate modeling to obtain accurate credible intervals for MD observables. Our results represent a step towards accurate UQ that is of vital importance for trustworthy NN potential-based MD simulations required for decision-making in practice.

An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore, this paper introduces VeML, a Version management system dedicated to end-to-end ML Lifecycle. Our system tackles several crucial problems that other systems have not solved. First, we address the high cost of building an ML lifecycle, especially for large-scale and high-dimensional dataset. We solve this problem by proposing to transfer the lifecycle of similar datasets managed in our system to the new training data. We design an algorithm based on the core set to compute similarity for large-scale, high-dimensional data efficiently. Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild. Our system helps to detect this mismatch without getting labeled data from testing data and rebuild the ML lifecycle for a new data version. To demonstrate our contributions, we conduct experiments on real-world, large-scale datasets of driving images and spatiotemporal sensor data and show promising results.

Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application. Traditional FTSF is regarded as an expert system which leads to loss of the ability to recognize undefined features. The mentioned is the main reason for poor forecasting with FTSF. To solve the problem, the proposed model Differential Fuzzy Convolutional Neural Network (DFCNN) utilizes a convolution neural network to re-implement FTSF with learnable ability. DFCNN is capable of recognizing potential information and improving forecasting accuracy. Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system. At the same time, FTSF usually cannot achieve satisfactory performance of non-stationary time series due to the trend of non-stationary time series. The trend of non-stationary time series causes the fuzzy set established by FTSF to be invalid and causes the forecasting to fail. DFCNN utilizes the Difference algorithm to weaken the non-stationary of time series so that DFCNN can forecast the non-stationary time series with a low error that FTSF cannot forecast in satisfactory performance. After the mass of experiments, DFCNN has an excellent prediction effect, which is ahead of the existing FTSF and common time series forecasting algorithms. Finally, DFCNN provides further ideas for improving FTSF and holds continued research value.

The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs are implemented on computer hardware, but also on unconventional physical substrates such as mechanical oscillators, spins, or bacteria often summarized as physical reservoir computing. Here we demonstrate physical reservoir computing with a synthetic active microparticle system that self-organizes from an active and passive component into inherently noisy nonlinear dynamical units. The self-organization and dynamical response of the unit is the result of a delayed propulsion of the microswimmer to a passive target. A reservoir of such units with a self-coupling via the delayed response can perform predictive tasks despite the strong noise resulting from Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce a special architecture that uses historical reservoir states for output. Our results pave the way for the study of information processing in synthetic self-organized active particle systems.

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.

Constraint programming is known for being an efficient approach for solving combinatorial problems. Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. To the best of our knowledge, it is still an open research question. Although several generic variable-selection heuristics are available in the literature, the options for a generic value-selection heuristic are more scarce. In this paper, we propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network architecture. Experiments on graph coloring, maximum independent set, and maximum cut problems show that our framework is able to find better solutions close to optimality without requiring a large amounts of backtracks while being generic.

Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent: The outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task's causal structure, as opposed to existing node embeddings, e.g., graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.

This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using non-idealized LR and HR pairs resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics, and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of non-idealized LR fields resulting from internal variability. Furthermore, we conduct a spectra-based feature-importance experiment allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.

In this work we propose an algorithm for trace recovery from stochastically known logs, a setting that is becoming more common with the increasing number of sensors and predictive models that generate uncertain data. The suggested approach calculates the conformance between a process model and a stochastically known trace and recovers the best alignment within this stochastic trace as the true trace. The paper offers an analysis of the impact of various cost models on trace recovery accuracy and makes use of a product multi-graph to compare alternative trace recovery options. The average accuracy of our approach, evaluated using two publicly available datasets, is impressive, with an average recovery accuracy score of 90-97%, significantly improving a common heuristic that chooses the most likely value for each uncertain activity. We believe that the effectiveness of the proposed algorithm in recovering correct traces from stochastically known logs may be a powerful aid for developing credible decision-making tools in uncertain settings.

Recognition problems in long-tailed data, where the sample size per class is heavily skewed, have recently gained importance because the distribution of the sample size per class in a dataset is generally exponential unless the sample size is intentionally adjusted. Various approaches have been devised to address these problems. Recently, weight balancing, which combines well-known classical regularization techniques with two-stage training, has been proposed. Despite its simplicity, it is known for its high performance against existing methods devised in various ways. However, there is a lack of understanding as to why this approach is effective for long-tailed data. In this study, we analyze the method focusing on neural collapse and cone effect at each training stage and find that it can be decomposed into the increase in Fisher's discriminant ratio of the feature extractor caused by weight decay and cross entropy loss and implicit logit adjustment caused by weight decay and class-balanced loss. Our analysis shows that the training method can be further simplified by reducing the number of training stages to one while increasing accuracy.

Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with unseen samples. As a result, a reliable deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. Despite various attempts to enhance cross-dataset generalization, the problem remains challenging, particularly when testing against common post-processing perturbations, such as video compression or blur. Hence, this study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability, withstanding common corruptions and enabling feature explainability. The framework comprises three key components: a feature extractor based on vision Transformer architecture that is pre-trained via self-supervised contrastive learning methodology, a graph convolution network coupled with a Transformer discriminator, and a graph Transformer relevancy map that provides a better understanding of manipulated regions and further explains the model's decision. To assess the effectiveness of the proposed framework, several challenging experiments are conducted, including in-data distribution performance, cross-dataset, cross-manipulation generalization, and robustness against common post-production perturbations. The results achieved demonstrate the remarkable effectiveness of the proposed deepfake detection framework, surpassing the current state-of-the-art approaches.

The Fourier neural operator (FNO) is a powerful technique for learning surrogate maps for partial differential equation (PDE) solution operators. For many real-world applications, which often require high-resolution data points, training time and memory usage are significant bottlenecks. While there are mixed-precision training techniques for standard neural networks, those work for real-valued datatypes on finite dimensions and therefore cannot be directly applied to FNO, which crucially operates in the (complex-valued) Fourier domain and in function spaces. On the other hand, since the Fourier transform is already an approximation (due to discretization error), we do not need to perform the operation at full precision. In this work, we (i) profile memory and runtime for FNO with full and mixed-precision training, (ii) conduct a study on the numerical stability of mixed-precision training of FNO, and (iii) devise a training routine which substantially decreases training time and memory usage (up to 34%), with little or no reduction in accuracy, on the Navier-Stokes and Darcy flow equations. Combined with the recently proposed tensorized FNO (Kossaifi et al., 2023), the resulting model has far better performance while also being significantly faster than the original FNO.

The Internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web. In particular, we show that a language model's ability to answer a fact-based question relates to how many documents associated with that question were seen during pre-training. We identify these relevant documents by entity linking pre-training datasets and counting documents that contain the same entities as a given question-answer pair. Our results demonstrate strong correlational and causal relationships between accuracy and relevant document count for numerous question answering datasets (e.g., TriviaQA), pre-training corpora (e.g., ROOTS), and model sizes (e.g., 176B parameters). Moreover, while larger models are better at learning long-tail knowledge, we estimate that today's models must be scaled by many orders of magnitude to reach competitive QA performance on questions with little support in the pre-training data. Finally, we show that retrieval-augmentation can reduce the dependence on relevant pre-training information, presenting a promising approach for capturing the long-tail.

Machine learning methods have significantly improved in their predictive capabilities, but at the same time they are becoming more complex and less transparent. As a result, explainers are often relied on to provide interpretability to these black-box prediction models. As crucial diagnostics tools, it is important that these explainers themselves are robust. In this paper we focus on one particular aspect of robustness, namely that an explainer should give similar explanations for similar data inputs. We formalize this notion by introducing and defining explainer astuteness, analogous to astuteness of prediction functions. Our formalism allows us to connect explainer robustness to the predictor's probabilistic Lipschitzness, which captures the probability of local smoothness of a function. We provide lower bound guarantees on the astuteness of a variety of explainers (e.g., SHAP, RISE, CXPlain) given the Lipschitzness of the prediction function. These theoretical results imply that locally smooth prediction functions lend themselves to locally robust explanations. We evaluate these results empirically on simulated as well as real datasets.

Solving parity games is a major building block for numerous applications in reactive program verification and synthesis. While they can be solved efficiently in practice, no known approach has a polynomial worst-case runtime complexity. We present a incomplete polynomial-time approach to determining the winning regions of parity games via graph neural networks. Our evaluation on 900 randomly generated parity games shows that this approach is effective and efficient in practice. It correctly determines the winning regions of $\sim$60\% of the games in our data set and only incurs minor errors in the remaining ones. We believe that this approach can be extended to efficiently solve parity games as well.

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST and CIFAR-10 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.

Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems in learning may encounter unstable generalization issues. Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data. To alleviate such limitations, we develop RetroPrompt with the motivation of decoupling knowledge from memorization to help the model strike a balance between generalization and memorization. In contrast with vanilla prompt learning, RetroPrompt constructs an open-book knowledge-store from training instances and implements a retrieval mechanism during the process of input, training and inference, thus equipping the model with the ability to retrieve related contexts from the training corpus as cues for enhancement. Extensive experiments demonstrate that RetroPrompt can obtain better performance in both few-shot and zero-shot settings. Besides, we further illustrate that our proposed RetroPrompt can yield better generalization abilities with new datasets. Detailed analysis of memorization indeed reveals RetroPrompt can reduce the reliance of language models on memorization; thus, improving generalization for downstream tasks. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/RetroPrompt.

Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains towards a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. The implementation code of CoTMix is available at \href{https://github.com/emadeldeen24/CoTMix}{github.com/emadeldeen24/CoTMix}.

Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis techniques for feature extraction, and even differ on what they consider a malware family. As a consequence, our community still lacks an understanding of malware classification results: whether they are tied to the nature and distribution of the collected dataset, to what extent the number of families and samples in the training dataset influence performance, and how well static and dynamic features complement each other. This work sheds light on those open questions. by investigating the key factors influencing ML-based malware detection and classification. For this, we collect the largest balanced malware dataset so far with 67K samples from 670 families (100 samples each), and train state-of-the-art models for malware detection and family classification using our dataset. Our results reveal that static features perform better than dynamic features, and that combining both only provides marginal improvement over static features. We discover no correlation between packing and classification accuracy, and that missing behaviors in dynamically-extracted features highly penalize their performance. We also demonstrate how a larger number of families to classify make the classification harder, while a higher number of samples per family increases accuracy. Finally, we find that models trained on a uniform distribution of samples per family better generalize on unseen data.

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed nature. Although hydrodynamic theories help us better understand the physical basis of this phenomenon, identifying a sufficient set of local interactions that shape, regulate, and sustain self-organized structures in active particle systems remains challenging. We investigate a classic hydrodynamic model of self-propelled particles that produces a wide variety of patterns, like asters and moving density bands. Our data-driven analysis shows that propagating bands are formed by local alignment interactions driven by density gradients, while steady-state asters are shaped by a mechanism of splay-induced negative compressibility arising from strong particle interactions. Our method also reveals analogous physical principles of pattern formation in a system where the speed of the particle is influenced by local density. This demonstrates the ability of our method to reveal physical commonalities across models. The physical mechanisms inferred from the data are in excellent agreement with analytical scaling arguments and experimental observations.

Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work. In this work, we consider a family of linear transformations and study their effects on the ridge estimator in an over-parametrized linear regression setting. First, we show that transformations that preserve the labels of the data can improve estimation by enlarging the span of the training data. Second, we show that transformations that mix data can improve estimation by playing a regularization effect. Finally, we validate our theoretical insights on MNIST. Based on the insights, we propose an augmentation scheme that searches over the space of transformations by how uncertain the model is about the transformed data. We validate our proposed scheme on image and text datasets. For example, our method outperforms random sampling methods by 1.24% on CIFAR-100 using Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA Adversarial AutoAugment on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets.

Approaches to recommendation are typically evaluated in one of two ways: (1) via a (simulated) online experiment, often seen as the gold standard, or (2) via some offline evaluation procedure, where the goal is to approximate the outcome of an online experiment. Several offline evaluation metrics have been adopted in the literature, inspired by ranking metrics prevalent in the field of Information Retrieval. (Normalised) Discounted Cumulative Gain (nDCG) is one such metric that has seen widespread adoption in empirical studies, and higher (n)DCG values have been used to present new methods as the state-of-the-art in top-$n$ recommendation for many years. Our work takes a critical look at this approach, and investigates when we can expect such metrics to approximate the gold standard outcome of an online experiment. We formally present the assumptions that are necessary to consider DCG an unbiased estimator of online reward and provide a derivation for this metric from first principles, highlighting where we deviate from its traditional uses in IR. Importantly, we show that normalising the metric renders it inconsistent, in that even when DCG is unbiased, ranking competing methods by their normalised DCG can invert their relative order. Through a correlation analysis between off- and on-line experiments conducted on a large-scale recommendation platform, we show that our unbiased DCG estimates strongly correlate with online reward, even when some of the metric's inherent assumptions are violated. This statement no longer holds for its normalised variant, suggesting that nDCG's practical utility may be limited.

It has long been believed that the brain is highly modular both in terms of structure and function, although recent evidence has led some to question the extent of both types of modularity. We used artificial neural networks to test the hypothesis that structural modularity is sufficient to guarantee functional specialization, and find that in general, this doesn't necessarily hold except at extreme levels. We then systematically tested which features of the environment and network do lead to the emergence of specialization. We used a simple toy environment, task and network, allowing us precise control, and show that in this setup, several distinct measures of specialization give qualitatively similar results. We further find that (1) specialization can only emerge in environments where features of that environment are meaningfully separable, (2) specialization preferentially emerges when the network is strongly resource-constrained, and (3) these findings are qualitatively similar across different network architectures, but the quantitative relationships depends on the architecture type. Finally, we show that functional specialization varies dynamically across time, and demonstrate that these dynamics depend on both the timing and bandwidth of information flow in the network. We conclude that a static notion of specialization, based on structural modularity, is likely too simple a framework for understanding intelligent systems in situations of real-world complexity. We propose that thoroughly stress testing candidate definitions of functional modularity in simplified scenarios before extending to more complex data, network models and electrophysiological recordings is likely to be a fruitful approach.

Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification, especially when the environment's dynamics are only partially known. This paper proposes a novel pipeline for learning non-Markovian task specifications as succinct finite-state `task automata' from episodes of agent experience within unknown environments. We leverage two key algorithmic insights. First, we learn a product MDP, a model composed of the specification's automaton and the environment's MDP (both initially unknown), by treating the product MDP as a partially observable MDP and using the well-known Baum-Welch algorithm for learning hidden Markov models. Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP. Our learnt task automaton enables the decomposition of a task into its constituent sub-tasks, which improves the rate at which an RL agent can later synthesise an optimal policy. It also provides an interpretable encoding of high-level environmental and task features, so a human can readily verify that the agent has learnt coherent tasks with no misspecifications. In addition, we take steps towards ensuring that the learnt automaton is environment-agnostic, making it well-suited for use in transfer learning. Finally, we provide experimental results compared with two baselines to illustrate our algorithm's performance in different environments and tasks.

Recognizing handwritten digits is a challenging task primarily due to the diversity of writing styles and the presence of noisy images. The widely used MNIST dataset, which is commonly employed as a benchmark for this task, includes distorted digits with irregular shapes, incomplete strokes, and varying skew in both the training and testing datasets. Consequently, these factors contribute to reduced accuracy in digit recognition. To overcome this challenge, we propose a two-stage deep learning approach. In the first stage, we create a simple neural network to identify distorted digits within the training set. This model serves to detect and filter out such distorted and ambiguous images. In the second stage, we exclude these identified images from the training dataset and proceed to retrain the model using the filtered dataset. This process aims to improve the classification accuracy and confidence levels while mitigating issues of underfitting and overfitting. Our experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy rate of over 99.5% on the testing dataset. This significant improvement showcases the potential of our method in enhancing digit classification accuracy. In our future work, we intend to explore the scalability of this approach and investigate techniques to further enhance accuracy by reducing the size of the training data.

There is a growing awareness of the harmful effects of distribution shift on the performance of deployed machine learning models. Consequently, there is a growing interest in detecting these shifts before associated costs have time to accumulate. However, desiderata of crucial importance to the practicable deployment of sequential shift detectors are typically overlooked by existing works, precluding their widespread adoption. We identify three such desiderata, highlight existing works relevant to their satisfaction, and recommend impactful directions for future research.

Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild". Our survey of real spreadsheet-tables and web-tables shows that over 30% of such tables do not conform to the relational standard, for which complex table-restructuring transformations are needed before these tables can be queried easily using SQL-based analytics tools. Unfortunately, the required transformations are non-trivial to program, which has become a substantial pain point for technical and non-technical users alike, as evidenced by large numbers of forum questions in places like StackOverflow and Excel/Tableau forums. We develop an Auto-Tables system that can automatically synthesize pipelines with multi-step transformations (in Python or other languages), to transform non-relational tables into standard relational forms for downstream analytics, obviating the need for users to manually program transformations. We compile an extensive benchmark for this new task, by collecting 194 real test cases from user spreadsheets and online forums. Our evaluation suggests that Auto-Tables can successfully synthesize transformations for over 70% of test cases at interactive speeds, without requiring any input from users, making this an effective tool for both technical and non-technical users to prepare data for analytics.

State-of-the-art neural networks require extreme computational power to train. It is therefore natural to wonder whether they are optimally trained. Here we apply a recent advancement in stochastic thermodynamics which allows bounding the speed at which one can go from the initial weight distribution to the final distribution of the fully trained network, based on the ratio of their Wasserstein-2 distance and the entropy production rate of the dynamical process connecting them. Considering both gradient-flow and Langevin training dynamics, we provide analytical expressions for these speed limits for linear and linearizable neural networks e.g. Neural Tangent Kernel (NTK). Remarkably, given some plausible scaling assumptions on the NTK spectra and spectral decomposition of the labels -- learning is optimal in a scaling sense. Our results are consistent with small-scale experiments with Convolutional Neural Networks (CNNs) and Fully Connected Neural networks (FCNs) on CIFAR-10, showing a short highly non-optimal regime followed by a longer optimal regime.

As public consciousness regarding the collection and use of personal information by corporations grows, it is of increasing importance that consumers be active participants in the curation of corporate datasets. In light of this, data governance frameworks such as the General Data Protection Regulation (GDPR) have outlined the right to be forgotten as a key principle allowing individuals to request that their personal data be deleted from the databases and models used by organizations. To achieve forgetting in practice, several machine unlearning methods have been proposed to address the computational inefficiencies of retraining a model from scratch with each unlearning request. While efficient online alternatives to retraining, it is unclear how these methods impact other properties critical to real-world applications, such as fairness. In this work, we propose the first fair machine unlearning method that can provably and efficiently unlearn data instances while preserving group fairness. We derive theoretical results which demonstrate that our method can provably unlearn data instances while maintaining fairness objectives. Extensive experimentation with real-world datasets highlight the efficacy of our method at unlearning data instances while preserving fairness.

Handwriting recognition is a challenging and critical problem in the fields of pattern recognition and machine learning, with applications spanning a wide range of domains. In this paper, we focus on the specific issue of recognizing offline Arabic handwritten text. Existing approaches typically utilize a combination of convolutional neural networks for image feature extraction and recurrent neural networks for temporal modeling, with connectionist temporal classification used for text generation. However, these methods suffer from a lack of parallelization due to the sequential nature of recurrent neural networks. Furthermore, these models cannot account for linguistic rules, necessitating the use of an external language model in the post-processing stage to boost accuracy. To overcome these issues, we introduce two alternative architectures, namely the Transformer Transducer and the standard sequence-to-sequence Transformer, and compare their performance in terms of accuracy and speed. Our approach can model language dependencies and relies only on the attention mechanism, thereby making it more parallelizable and less complex. We employ pre-trained Transformers for both image understanding and language modeling. Our evaluation on the Arabic KHATT dataset demonstrates that our proposed method outperforms the current state-of-the-art approaches for recognizing offline Arabic handwritten text.

Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance

We present a new large-scale emotion-labeled symbolic music dataset consisting of 12k MIDI songs. To create this dataset, we first trained emotion classification models on the GoEmotions dataset, achieving state-of-the-art results with a model half the size of the baseline. We then applied these models to lyrics from two large-scale MIDI datasets. Our dataset covers a wide range of fine-grained emotions, providing a valuable resource to explore the connection between music and emotions and, especially, to develop models that can generate music based on specific emotions. Our code for inference, trained models, and datasets are available online.

In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which frequently appears in many data mining and machine learning problems with a hierarchical structure such as distributionally robust FL and model-agnostic meta learning (MAML). Moreover, we study the convergence analysis of our ComFedL algorithm under some mild conditions, and prove that it achieves a convergence rate of $O(\frac{1}{\sqrt{T}})$, where $T$ denotes the number of iteration. To the best of our knowledge, our new Compositional FL framework is the first work to bridge federated learning with composition stochastic optimization. In particular, we first transform the distributionally robust FL (i.e., a minimax optimization problem) into a simple composition optimization problem by using KL divergence regularization. At the same time, we also first transform the distribution-agnostic MAML problem (i.e., a minimax optimization problem) into a simple yet effective composition optimization problem. Finally, we apply two popular machine learning tasks, i.e., distributionally robust FL and MAML to demonstrate the effectiveness of our algorithm.

Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house. Efficient and accurate load monitoring facilitates user profile establishment, intelligent household energy management, and peak load shifting. This is beneficial for both the end-users and utilities by improving the overall efficiency of a power distribution network. Existing approaches mainly focus on developing an individual model for each appliance. Those approaches typically rely on a large amount of household-labeled data which is hard to collect. In this paper, we propose a multi-appliance-task framework with a training-efficient sample augmentation (SA) scheme that boosts the disaggregation performance with limited labeled data. For each appliance, we develop a shared-hierarchical split structure for its regression and classification tasks. In addition, we also propose a two-dimensional attention mechanism in order to capture spatio-temporal correlations among all appliances. With only one-day training data and limited appliance operation profiles, the proposed SA algorithm can achieve comparable test performance to the case of training with the full dataset. Finally, simulation results show that our proposed approach features a significantly improved performance over many baseline models. The relative errors can be reduced by more than 50\% on average. The codes of this work are available at https://github.com/jxiong22/MATNilm

This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailing models such as SASRec and GRU4Rec+, TRON integrates top-k negative sampling and listwise loss functions to enhance its recommendation accuracy. Evaluations on relevant large-scale e-commerce datasets show that TRON improves upon the recommendation quality of current methods while maintaining training speeds similar to SASRec. A live A/B test yielded an 18.14% increase in click-through rate over SASRec, highlighting the potential of TRON in practical settings. For further research, we provide access to our source code at https://github.com/otto-de/TRON and an anonymized dataset at https://github.com/otto-de/recsys-dataset.

Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state. A major challenge for making accurate and robust action predictions is the problem of confounding, which if left untreated can lead to prediction errors. The partially observable Markov decision process (POMDP) is a widely-used framework to model these stochastic and partially-observable decision-making problems. However, due to a lack of explicit causal semantics, POMDP planning methods are prone to confounding bias and thus in the presence of unobserved confounders may produce underperforming policies. This paper presents a novel causally-informed extension of "anytime regularized determinized sparse partially observable tree" (AR-DESPOT), a modern anytime online POMDP planner, using causal modelling and inference to eliminate errors caused by unmeasured confounder variables. We further propose a method to learn offline the partial parameterisation of the causal model for planning, from ground truth model data. We evaluate our methods on a toy problem with an unobserved confounder and show that the learned causal model is highly accurate, while our planning method is more robust to confounding and produces overall higher performing policies than AR-DESPOT.

With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision. However, in 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training. In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model. We propose to generate view images from different instructed poses via the cross-attention mechanism as the pre-training scheme. Generating view images has more precise supervision than its point cloud counterpart, thus assisting 3D backbones to have a finer comprehension of the geometrical structure and stereoscopic relations of the point cloud. Experimental results have proved the superiority of our proposed 3D-to-2D generative pre-training over previous pre-training methods. Our method is also effective in boosting the performance of architecture-oriented approaches, achieving state-of-the-art performance when fine-tuning on ScanObjectNN classification and ShapeNetPart segmentation tasks. Code is available at https://github.com/wangzy22/TAP.

The continuous dynamics of natural systems has been effectively modelled using Neural Ordinary Differential Equations (Neural ODEs). However, for accurate and meaningful predictions, it is crucial that the models follow the underlying rules or laws that govern these systems. In this work, we propose a self-adaptive penalty algorithm for Neural ODEs to enable modelling of constrained natural systems. The proposed self-adaptive penalty function can dynamically adjust the penalty parameters. The explicit introduction of prior knowledge helps to increase the interpretability of Neural ODE -based models. We validate the proposed approach by modelling three natural systems with prior knowledge constraints: population growth, chemical reaction evolution, and damped harmonic oscillator motion. The numerical experiments and a comparison with other penalty Neural ODE approaches and \emph{vanilla} Neural ODE, demonstrate the effectiveness of the proposed self-adaptive penalty algorithm for Neural ODEs in modelling constrained natural systems. Moreover, the self-adaptive penalty approach provides more accurate and robust models with reliable and meaningful predictions.